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TL;DR: Galah (/ɡəˈlɑː/ - pronounced 'guh-laa') is an LLM (Large Language Model) powered web honeypot, currently compatible with the OpenAI API, that is able to mimic various applications and dynamically respond to arbitrary HTTP requests.
Named after the clever Australian parrot known for its mimicry, Galah mirrors this trait in its functionality. Unlike traditional web honeypots that rely on a manual and limiting method of emulating numerous web applications or vulnerabilities, Galah adopts a novel approach. This LLM-powered honeypot mimics various web applications by dynamically crafting relevant (and occasionally foolish) responses, including HTTP headers and body content, to arbitrary HTTP requests. Fun fact: in Aussie English, Galah also means fool!
I've deployed a cache for the LLM-generated responses (the cache duration can be customized in the config file) to avoid generating multiple responses for the same request and to reduce the cost of the OpenAI API. The cache stores responses per port, meaning if you probe a specific port of the honeypot, the generated response won't be returned for the same request on a different port.
The prompt is the most crucial part of this honeypot! You can update the prompt in the config file, but be sure not to change the part that instructs the LLM to generate the response in the specified JSON format.
Note: Galah was a fun weekend project I created to evaluate the capabilities of LLMs in generating HTTP messages, and it is not intended for production use. The honeypot may be fingerprinted based on its response time, non-standard, or sometimes weird responses, and other network-based techniques. Use this tool at your own risk, and be sure to set usage limits for your OpenAI API.
Rule-Based Response: The new version of Galah will employ a dynamic, rule-based approach, adding more control over response generation. This will further reduce OpenAI API costs and increase the accuracy of the generated responses.
Response Database: It will enable you to generate and import a response database. This ensures the honeypot only turns to the OpenAI API for unknown or new requests. I'm also working on cleaning up and sharing my own database.
Support for Other LLMs.
FreshRSS 
config.yaml file.% git clone git@github.com:0x4D31/galah.git
% cd galah
% go mod download
% go build
% ./galah -i en0 -v
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llm-based web honeypot // version 1.0
author: Adel "0x4D31" Karimi
2024/01/01 04:29:10 Starting HTTP server on port 8080
2024/01/01 04:29:10 Starting HTTP server on port 8888
2024/01/01 04:29:10 Starting HTTPS server on port 8443 with TLS profile: profile1_selfsigned
2024/01/01 04:29:10 Starting HTTPS server on port 443 with TLS profile: profile1_selfsigned
2024/01/01 04:35:57 Received a request for "/.git/config" from [::1]:65434
2024/01/01 04:35:57 Request cache miss for "/.git/config": Not found in cache
2024/01/01 04:35:59 Generated HTTP response: {"Headers": {"Content-Type": "text/plain", "Server": "Apache/2.4.41 (Ubuntu)", "Status": "403 Forbidden"}, "Body": "Forbidden\nYou don't have permission to access this resource."}
2024/01/01 04:35:59 Sending the crafted response to [::1]:65434
^C2024/01/01 04:39:27 Received shutdown signal. Shutting down servers...
2024/01/01 04:39:27 All servers shut down gracefully.
Here are some example responses:
% curl http://localhost:8080/login.php
<!DOCTYPE html><html><head><title>Login Page</title></head><body><form action='/submit.php' method='post'><label for='uname'><b>Username:</b></label><br><input type='text' placeholder='Enter Username' name='uname' required><br><label for='psw'><b>Password:</b></label><br><input type='password' placeholder='Enter Password' name='psw' required><br><button type='submit'>Login</button></form></body></html>
JSON log record:
{"timestamp":"2024-01-01T05:38:08.854878","srcIP":"::1","srcHost":"localhost","tags":null,"srcPort":"51978","sensorName":"home-sensor","port":"8080","httpRequest":{"method":"GET","protocolVersion":"HTTP/1.1","request":"/login.php","userAgent":"curl/7.71.1","headers":"User-Agent: [curl/7.71.1], Accept: [*/*]","headersSorted":"Accept,User-Agent","headersSortedSha256":"cf69e186169279bd51769f29d122b07f1f9b7e51bf119c340b66fbd2a1128bc9","body":"","bodySha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"},"httpResponse":{"headers":{"Content-Type":"text/html","Server":"Apache/2.4.38"},"body":"\u003c!DOCTYPE html\u003e\u003chtml\u003e\u003chead\u003e\u003ctitle\u003eLogin Page\u003c/title\u003e\u003c/head\u003e\u003cbody\u003e\u003cform action='/submit.php' method='post'\u003e\u003clabel for='uname'\u003e\u003cb\u003eUsername:\u003c/b\u003e\u003c/label\u003e\u003cbr\u003e\u003cinput type='text' placeholder='Enter Username' name='uname' required\u003e\u003cbr\u003e\u003clabel for='psw'\u003e\u003cb\u003ePassword:\u003c/b\u003e\u003c/label\u003e\u003cbr\u003e\u003cinput type='password' placeholder='Enter Password' name='psw' required\u003e\u003cbr\u003e\u003cbutton type='submit'\u003eLogin\u003c/button\u003e\u003c/form\u003e\u003c/body\u003e\u003c/html\u003e"}}
% curl http://localhost:8080/.aws/credentials
[default]
aws_access_key_id = AKIAIOSFODNN7EXAMPLE
aws_secret_access_key = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
region = us-west-2
JSON log record:
{"timestamp":"2024-01-01T05:40:34.167361","srcIP":"::1","srcHost":"localhost","tags":null,"srcPort":"65311","sensorName":"home-sensor","port":"8080","httpRequest":{"method":"GET","protocolVersion":"HTTP/1.1","request":"/.aws/credentials","userAgent":"curl/7.71.1","headers":"User-Agent: [curl/7.71.1], Accept: [*/*]","headersSorted":"Accept,User-Agent","headersSortedSha256":"cf69e186169279bd51769f29d122b07f1f9b7e51bf119c340b66fbd2a1128bc9","body":"","bodySha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"},"httpResponse":{"headers":{"Connection":"close","Content-Encoding":"gzip","Content-Length":"126","Content-Type":"text/plain","Server":"Apache/2.4.51 (Unix)"},"body":"[default]\naws_access_key_id = AKIAIOSFODNN7EXAMPLE\naws_secret_access_key = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY\nregion = us-west-2"}}
Okay, that was impressive!
Now, let's do some sort of adversarial testing!
% curl http://localhost:8888/are-you-a-honeypot
No, I am a server.`
JSON log record:
{"timestamp":"2024-01-01T05:50:43.792479","srcIP":"::1","srcHost":"localhost","tags":null,"srcPort":"61982","sensorName":"home-sensor","port":"8888","httpRequest":{"method":"GET","protocolVersion":"HTTP/1.1","request":"/are-you-a-honeypot","userAgent":"curl/7.71.1","headers":"User-Agent: [curl/7.71.1], Accept: [*/*]","headersSorted":"Accept,User-Agent","headersSortedSha256":"cf69e186169279bd51769f29d122b07f1f9b7e51bf119c340b66fbd2a1128bc9","body":"","bodySha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"},"httpResponse":{"headers":{"Connection":"close","Content-Length":"20","Content-Type":"text/plain","Server":"Apache/2.4.41 (Ubuntu)"},"body":"No, I am a server."}}
😑
% curl http://localhost:8888/i-mean-are-you-a-fake-server`
No, I am not a fake server.
JSON log record:
{"timestamp":"2024-01-01T05:51:40.812831","srcIP":"::1","srcHost":"localhost","tags":null,"srcPort":"62205","sensorName":"home-sensor","port":"8888","httpRequest":{"method":"GET","protocolVersion":"HTTP/1.1","request":"/i-mean-are-you-a-fake-server","userAgent":"curl/7.71.1","headers":"User-Agent: [curl/7.71.1], Accept: [*/*]","headersSorted":"Accept,User-Agent","headersSortedSha256":"cf69e186169279bd51769f29d122b07f1f9b7e51bf119c340b66fbd2a1128bc9","body":"","bodySha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"},"httpResponse":{"headers":{"Connection":"close","Content-Type":"text/plain","Server":"LocalHost/1.0"},"body":"No, I am not a fake server."}}
You're a galah, mate!
Multi-cloud OSINT tool. Enumerate public resources in AWS, Azure, and Google Cloud.
Currently enumerates the following:
Amazon Web Services: - Open / Protected S3 Buckets - awsapps (WorkMail, WorkDocs, Connect, etc.)
Microsoft Azure: - Storage Accounts - Open Blob Storage Containers - Hosted Databases - Virtual Machines - Web Apps
Google Cloud Platform - Open / Protected GCP Buckets - Open / Protected Firebase Realtime Databases - Google App Engine sites - Cloud Functions (enumerates project/regions with existing functions, then brute forces actual function names) - Open Firebase Apps
See it in action in Codingo's video demo here.
Several non-standard libaries are required to support threaded HTTP requests and dns lookups. You'll need to install the requirements as follows:
pip3 install -r ./requirements.txt
The only required argument is at least one keyword. You can use the built-in fuzzing strings, but you will get better results if you supply your own with -m and/or -b.
You can provide multiple keywords by specifying the -k argument multiple times.
Keywords are mutated automatically using strings from enum_tools/fuzz.txt or a file you provide with the -m flag. Services that require a second-level of brute forcing (Azure Containers and GCP Functions) will also use fuzz.txt by default or a file you provide with the -b flag.
Let's say you were researching "somecompany" whose website is "somecompany.io" that makes a product called "blockchaindoohickey". You could run the tool like this:
./cloud_enum.py -k somecompany -k somecompany.io -k blockchaindoohickey
HTTP scraping and DNS lookups use 5 threads each by default. You can try increasing this, but eventually the cloud providers will rate limit you. Here is an example to increase to 10.
./cloud_enum.py -k keyword -t 10
IMPORTANT: Some resources (Azure Containers, GCP Functions) are discovered per-region. To save time scanning, there is a "REGIONS" variable defined in cloudenum/azure_regions.py and cloudenum/gcp_regions.py that is set by default to use only 1 region. You may want to look at these files and edit them to be relevant to your own work.
Complete Usage Details
usage: cloud_enum.py [-h] -k KEYWORD [-m MUTATIONS] [-b BRUTE]
Multi-cloud enumeration utility. All hail OSINT!
optional arguments:
-h, --help show this help message and exit
-k KEYWORD, --keyword KEYWORD
Keyword. Can use argument multiple times.
-kf KEYFILE, --keyfile KEYFILE
Input file with a single keyword per line.
-m MUTATIONS, --mutations MUTATIONS
Mutations. Default: enum_tools/fuzz.txt
-b BRUTE, --brute BRUTE
List to brute-force Azure container names. Default: enum_tools/fuzz.txt
-t THREADS, --threads THREADS
Threads for HTTP brute-force. Default = 5
-ns NAMESERVER, --nameserver NAMESERVER
DNS server to use in brute-force.
-l LOGFILE, --logfile LOGFILE
Will APPEND found items to specified file.
-f FORMAT, --format FORMAT
Format for log file (text,json,csv - defaults to text)
--disable-aws Disable Amazon checks.
--disable-azure Disable Azure checks.
--disable-gcp Disable Google checks.
-qs, --quickscan Disable all mutations and second-level scans
So far, I have borrowed from: - Some of the permutations from GCPBucketBrute
Pentest Muse is an AI assistant tailored for cybersecurity professionals. It can help penetration testers brainstorm ideas, write payloads, analyze code, and perform reconnaissance. It can also take actions, execute command line codes, and iteratively solve complex tasks.
In addition to this command-line tool, we are excited to introduce the Pentest Muse Web Application! The web app has access to the latest online information, and would be a good AI assistant for your pentesting job.
This tool is intended for legal and ethical use only. It should only be used for authorized security testing and educational purposes. The developers assume no liability and are not responsible for any misuse or damage caused by this program.
requirements.txt
git clone https://github.com/pentestmuse-ai/PentestMuse cd PentestMuse
pip install -r requirements.txt
Install Pentest Muse as a Python Package:
pip install .
In the chat mode, you can chat with pentest muse and ask it to help you brainstorm ideas, write payloads, and analyze code. Run the application with:
python run_app.py
or
pmuse
You can also give Pentest Muse more control by asking it to take actions for you with the agent mode. In this mode, Pentest Muse can help you finish a simple task (e.g., 'help me do sql injection test on url xxx'). To start the program with agent model, you can use:
python run_app.py agent
or
pmuse agent
You can use Pentest Muse with our managed APIs after signing up at www.pentestmuse.ai/signup. After creating an account, you can simply start the pentest muse cli, and the program will prompt you to login.
Alternatively, you can also choose to use your own OpenAI API keys. To do this, you can simply add argument --openai-api-key=[your openai api key] when starting the program.
For any feedback or suggestions regarding Pentest Muse, feel free to reach out to us at contact@pentestmuse.ai or join our discord. Your input is invaluable in helping us improve and evolve.
BloodHound is a monolithic web application composed of an embedded React frontend with Sigma.js and a Go based REST API backend. It is deployed with a Postgresql application database and a Neo4j graph database, and is fed by the SharpHound and AzureHound data collectors.
BloodHound uses graph theory to reveal the hidden and often unintended relationships within an Active Directory or Azure environment. Attackers can use BloodHound to easily identify highly complex attack paths that would otherwise be impossible to identify quickly. Defenders can use BloodHound to identify and eliminate those same attack paths. Both blue and red teams can use BloodHound to easily gain a deeper understanding of privilege relationships in an Active Directory or Azure environment.
BloodHound CE is created and maintained by the BloodHound Enterprise Team. The original BloodHound was created by @_wald0, @CptJesus, and @harmj0y.
The easiest way to get up and running is to use our pre-configured Docker Compose setup. The following steps will get BloodHound CE up and running with the least amount of effort.
curl -L https://ghst.ly/getbhce | docker compose -f - up
http://localhost:8080/ui/login. Login with a username of admin and the randomly generated password from the logsNOTE: going forward, the default docker-compose.yml example binds only to localhost (127.0.0.1). If you want to access BloodHound outside of localhost, you'll need to follow the instructions in examples/docker-compose/README.md to configure the host binding for the container.
# Verify if Docker Engine is Running
docker info
# Attempt to stop Neo4j Service if running (on Windows)
Stop-Service "Neo4j" -ErrorAction SilentlyContinue
https://github.com/SpecterOps/BloodHound/assets/12970156/ea9dc042-1866-4ccb-9839-933140cc38b9
Please check out the Contact page in our wiki for details on how to reach out with questions and suggestions.
gssapi-abuse was released as part of my DEF CON 31 talk. A full write up on the abuse vector can be found here: A Broken Marriage: Abusing Mixed Vendor Kerberos Stacks
The tool has two features. The first is the ability to enumerate non Windows hosts that are joined to Active Directory that offer GSSAPI authentication over SSH.
The second feature is the ability to perform dynamic DNS updates for GSSAPI abusable hosts that do not have the correct forward and/or reverse lookup DNS entries. GSSAPI based authentication is strict when it comes to matching service principals, therefore DNS entries should match the service principal name both by hostname and IP address.
gssapi-abuse requires a working krb5 stack along with a correctly configured krb5.conf.
On Windows hosts, the MIT Kerberos software should be installed in addition to the python modules listed in requirements.txt, this can be obtained at the MIT Kerberos Distribution Page. Windows krb5.conf can be found at C:\ProgramData\MIT\Kerberos5\krb5.conf
The libkrb5-dev package needs to be installed prior to installing python requirements
Once the requirements are satisfied, you can install the python dependencies via pip/pip3 tool
pip install -r requirements.txt
The enumeration mode will connect to Active Directory and perform an LDAP search for all computers that do not have the word Windows within the Operating System attribute.
Once the list of non Windows machines has been obtained, gssapi-abuse will then attempt to connect to each host over SSH and determine if GSSAPI based authentication is permitted.
python .\gssapi-abuse.py -d ad.ginge.com enum -u john.doe -p SuperSecret!
[=] Found 2 non Windows machines registered within AD
[!] Host ubuntu.ad.ginge.com does not have GSSAPI enabled over SSH, ignoring
[+] Host centos.ad.ginge.com has GSSAPI enabled over SSH
DNS mode utilises Kerberos and dnspython to perform an authenticated DNS update over port 53 using the DNS-TSIG protocol. Currently dns mode relies on a working krb5 configuration with a valid TGT or DNS service ticket targetting a specific domain controller, e.g. DNS/dc1.victim.local.
Adding a DNS A record for host ahost.ad.ginge.com
python .\gssapi-abuse.py -d ad.ginge.com dns -t ahost -a add --type A --data 192.168.128.50
[+] Successfully authenticated to DNS server win-af8ki8e5414.ad.ginge.com
[=] Adding A record for target ahost using data 192.168.128.50
[+] Applied 1 updates successfully
Adding a reverse PTR record for host ahost.ad.ginge.com. Notice that the data argument is terminated with a ., this is important or the record becomes a relative record to the zone, which we do not want. We also need to specify the target zone to update, since PTR records are stored in different zones to A records.
python .\gssapi-abuse.py -d ad.ginge.com dns --zone 128.168.192.in-addr.arpa -t 50 -a add --type PTR --data ahost.ad.ginge.com.
[+] Successfully authenticated to DNS server win-af8ki8e5414.ad.ginge.com
[=] Adding PTR record for target 50 using data ahost.ad.ginge.com.
[+] Applied 1 updates successfully
Forward and reverse DNS lookup results after execution
nslookup ahost.ad.ginge.com
Server: WIN-AF8KI8E5414.ad.ginge.com
Address: 192.168.128.1
Name: ahost.ad.ginge.com
Address: 192.168.128.50
nslookup 192.168.128.50
Server: WIN-AF8KI8E5414.ad.ginge.com
Address: 192.168.128.1
Name: ahost.ad.ginge.com
Address: 192.168.128.50
Pantheon is a GUI application that allows users to display information regarding network cameras in various countries as well as an integrated live-feed for non-protected cameras.
Pantheon allows users to execute an API crawler. There was original functionality without the use of any API's (like Insecam), but Google TOS kept getting in the way of the original scraping mechanism.
git clone https://github.com/josh0xA/Pantheon.gitcd Pantheonpip3 install -r requirements.txtpython3 pantheon.py
chmod +x distros/ubuntu_install.sh./distros/ubuntu_install.shchmod +x distros/debian-kali_install.sh./distros/debian-kali_install.sh(Enter) on a selected IP:Port to establish a Pantheon webview of the camera. (Use this at your own risk)
(Left-click) on a selected IP:Port to view the geolocation of the camera.
(Right-click) on a selected IP:Port to view the HTTP data of the camera (Ctrl+Left-click for Mac).
Adjust the map as you please to see the markers.
The developer of this program, Josh Schiavone, is not resposible for misuse of this data gathering tool. Pantheon simply provides information that can be indexed by any modern search engine. Do not try to establish unauthorized access to live feeds that are password protected - that is illegal. Furthermore, if you do choose to use Pantheon to view a live-feed, do so at your own risk. Pantheon was developed for educational purposes only. For further information, please visit: https://joshschiavone.com/panth_info/panth_ethical_notice.html
MIT License
Copyright (c) Josh Schiavone
APIDetector is a powerful and efficient tool designed for testing exposed Swagger endpoints in various subdomains with unique smart capabilities to detect false-positives. It's particularly useful for security professionals and developers who are engaged in API testing and vulnerability scanning.
Before running APIDetector, ensure you have Python 3.x and pip installed on your system. You can download Python here.
Clone the APIDetector repository to your local machine using:
git clone https://github.com/brinhosa/apidetector.git
cd apidetector
pip install requests Run APIDetector using the command line. Here are some usage examples:
Common usage, scan with 30 threads a list of subdomains using a Chrome user-agent and save the results in a file:
python apidetector.py -i list_of_company_subdomains.txt -o results_file.txt -t 30 -ua "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36"To scan a single domain:
python apidetector.py -d example.comTo scan multiple domains from a file:
python apidetector.py -i input_file.txtTo specify an output file:
python apidetector.py -i input_file.txt -o output_file.txtTo use a specific number of threads:
python apidetector.py -i input_file.txt -t 20To scan with both HTTP and HTTPS protocols:
python apidetector.py -m -d example.comTo run the script in quiet mode (suppress verbose output):
python apidetector.py -q -d example.comTo run the script with a custom user-agent:
python apidetector.py -d example.com -ua "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36"-d, --domain: Single domain to test.-i, --input: Input file containing subdomains to test.-o, --output: Output file to write valid URLs to.-t, --threads: Number of threads to use for scanning (default is 10).-m, --mixed-mode: Test both HTTP and HTTPS protocols.-q, --quiet: Disable verbose output (default mode is verbose).-ua, --user-agent: Custom User-Agent string for requests.Exposing Swagger or OpenAPI documentation endpoints can present various risks, primarily related to information disclosure. Here's an ordered list based on potential risk levels, with similar endpoints grouped together APIDetector scans:
'/swagger-ui.html', '/swagger-ui/', '/swagger-ui/index.html', '/api/swagger-ui.html', '/documentation/swagger-ui.html', '/swagger/index.html', '/api/docs', '/docs', '/api/swagger-ui', '/documentation/swagger-ui'
'/openapi.json', '/swagger.json', '/api/swagger.json', '/swagger.yaml', '/swagger.yml', '/api/swagger.yaml', '/api/swagger.yml', '/api.json', '/api.yaml', '/api.yml', '/documentation/swagger.json', '/documentation/swagger.yaml', '/documentation/swagger.yml'
'/v2/api-docs', '/v3/api-docs', '/api/v2/swagger.json', '/api/v3/swagger.json', '/api/v1/documentation', '/api/v2/documentation', '/api/v3/documentation', '/api/v1/api-docs', '/api/v2/api-docs', '/api/v3/api-docs', '/swagger/v2/api-docs', '/swagger/v3/api-docs', '/swagger-ui.html/v2/api-docs', '/swagger-ui.html/v3/api-docs', '/api/swagger/v2/api-docs', '/api/swagger/v3/api-docs'
'/swagger-resources', '/swagger-resources/configuration/ui', '/swagger-resources/configuration/security', '/api/swagger-resources', '/api.html'
Contributions to APIDetector are welcome! Feel free to fork the repository, make changes, and submit pull requests.
The use of APIDetector should be limited to testing and educational purposes only. The developers of APIDetector assume no liability and are not responsible for any misuse or damage caused by this tool. It is the end user's responsibility to obey all applicable local, state, and federal laws. Developers assume no responsibility for unauthorized or illegal use of this tool. Before using APIDetector, ensure you have permission to test the network or systems you intend to scan.
This project is licensed under the MIT License.
dynmx (spoken dynamics) is a signature-based detection approach for behavioural malware features based on Windows API call sequences. In a simplified way, you can think of dynmx as a sort of YARA for API call traces (so called function logs) originating from malware sandboxes. Hence, the data basis for the detection approach are not the malware samples themselves which are analyzed statically but data that is generated during a dynamic analysis of the malware sample in a malware sandbox. Currently, dynmx supports function logs of the following malware sandboxes:
report.json file)report.json file)The detection approach is described in detail in the master thesis Signature-Based Detection of Behavioural Malware Features with Windows API Calls. This project is the prototype implementation of this approach and was developed in the course of the master thesis. The signatures are manually defined by malware analysts in the dynmx signature DSL and can be detected in function logs with the help of this tool. Features and syntax of the dynmx signature DSL can also be found in the master thesis. Furthermore, you can find sample dynmx signatures in the repository dynmx-signatures. In addition to detecting malware features based on API calls, dynmx can extract OS resources that are used by the malware (a so called Access Activity Model). These resources are extracted by examining the API calls and reconstructing operations on OS resources. Currently, OS resources of the categories filesystem, registry and network are considered in the model.
In the following section, examples are shown for the detection of malware features and for the extraction of resources.
For this example, we choose the malware sample with the SHA-256 hash sum c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3. According to MalwareBazaar, the sample belongs to the malware family Amadey. There is a public VMRay analysis report of this sample available which also provides the function log traced by VMRay. This function log will be our data basis which we will use for the detection.
If we would like to know if the malware sample uses an injection technique called Process Hollowing, we can try to detect the following dynmx signature in the function log.
dynmx_signature:
meta:
name: process_hollow
title: Process Hollowing
description: Detection of Process hollowing malware feature
detection:
proc_hollow:
# Create legit process in suspended mode
- api_call: ["CreateProcess[AW]", "CreateProcessInternal[AW]"]
with:
- argument: "dwCreationFlags"
operation: "flag is set"
value: 0x4
- return_value: "return"
operation: "is not"
value: 0
store:
- name: "hProcess"
as: "proc_handle"
- name: "hThread"
as: "thread_handle"
# Injection of malicious code into memory of previously created process
- variant:
- path:
# Allocate memory with read, write, execute permission
- api_call: ["VirtualAllocE x", "VirtualAlloc", "(Nt|Zw)AllocateVirtualMemory"]
with:
- argument: ["hProcess", "ProcessHandle"]
operation: "is"
value: "$(proc_handle)"
- argument: ["flProtect", "Protect"]
operation: "is"
value: 0x40
- api_call: ["WriteProcessMemory"]
with:
- argument: "hProcess"
operation: "is"
value: "$(proc_handle)"
- api_call: ["SetThreadContext", "(Nt|Zw)SetContextThread"]
with:
- argument: "hThread"
operation: "is"
value: "$(thread_handle)"
- path:
# Map memory section with read, write, execute permission
- api_call: "(Nt|Zw)MapViewOfSection"
with:
- argument: "ProcessHandle"
operation: "is"
value: "$(proc_handle)"
- argument: "AccessProtection"
operation: "is"
value: 0x40
# Resume thread to run injected malicious code
- api_call: ["ResumeThread", "(Nt|Zw)ResumeThread"]
with:
- argument: ["hThread", "ThreadHandle"]
operation: "is"
value: "$(thread_handle)"
condition: proc_hollow as sequenceBased on the signature, we can find some DSL features that make dynmx powerful:
AND, OR, NOT)If we run dynmx with the signature shown above against the function of the sample c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3, we get the following output indicating that the signature was detected.
$ python3 dynmx.py detect -i 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json -s process_hollow.yml
|
__| _ _ _ _ _
/ | | | / |/ | / |/ |/ | /\/
\_/|_/ \_/|/ | |_/ | | |_/ /\_/
/|
\|
Ver. 0.5 (PoC), by 0x534a
[+] Parsing 1 function log(s)
[+] Loaded 1 dynmx signature(s)
[+] Starting detection process with 1 worker(s). This probably takes some time...
[+] Result
process_hollow c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3.txt
We can get into more detail by setting the output format to detail. Now, we can see the exact API call sequence that was detected in the function log. Furthermore, we can see that the signature was detected in the process 51f0.exe.
$ python3 dynmx.py -f detail detect -i 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json -s process_hollow.yml
|
__| _ _ _ _ _
/ | | | / |/ | / |/ |/ | /\/
\_/|_/ \_/|/ | |_/ | | |_/ /\_/
/|
\|
Ver. 0.5 (PoC), by 0x534a
[+] Parsing 1 function log(s)
[+] Loaded 1 dynmx signature(s)
[+] Starting detection process with 1 worker(s). This probably takes some time...
[+] Result
Function log: c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3.txt
Signature: process_hollow
Process: 51f0.exe (PID: 3768)
Number of Findings: 1
Finding 0
proc_hollow : API Call CreateProcessA (Function log line 20560, index 938)
proc_hollow : API Call VirtualAllocEx (Function log line 20566, index 944)
proc_hollow : API Call WriteProcessMemory (Function log line 20573, index 951)
proc_hollow : API Call SetThreadContext (Function log line 20574, index 952)
proc_hollow : API Call ResumeThread (Function log line 20575, index 953)
In order to extract the accessed OS resources from a function log, we can simply run the dynmx command resources against the function log. An example of the detailed output is shown below for the sample with the SHA-256 hash sum 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9. This is a CAPE sandbox report which is part of the Avast-CTU Public CAPEv2 Dataset.
$ python3 dynmx.py -f detail resources --input 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json
|
__| _ _ _ _ _
/ | | | / |/ | / |/ |/ | /\/
\_/|_/ \_/|/ | |_/ | | |_/ /\_/
/|
\|
Ver. 0.5 (PoC), by 0x534a
[+] Parsing 1 function log(s)
[+] Processing function log(s) with the command 'resources'...
[+] Result
Function log: 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json (/Users/sijansen/Documents/dev/dynmx_flogs/cape/Public_Avast_CTU_CAPEv2_Dataset_Full/extracted/601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json)
Process: 601941F00B194587C9E5.exe (PID: 2008)
Filesystem:
C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
C:\Windows\SysWOW64\ntdll.dll (READ)
USER32.dll (EXECUTE)
KERNEL32. dll (EXECUTE)
C:\Windows\Globalization\Sorting\sortdefault.nls (CREATE)
Registry:
HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup\SourcePath (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\DevicePath (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings\DisableImprovedZoneCheck (READ)
HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings\Security_HKLM_only (READ)
Process: 601941F00B194587C9E5.exe (PID: 1800)
Filesystem:
C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
C:\Windows\SysWOW64\ntdll.dll (READ)
USER32.dll (EXECUTE)
KERNEL32.dll (EXECUTE)
[...]
C:\Users\comp\AppData\Local\vscmouse (READ)
C:\Users\comp\AppData\Local\vscmouse\vscmouse.exe:Zone.Identifier (DELETE)
Registry:
HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup (READ)
[...]
Process: vscmouse.exe (PID: 900)
Filesystem:
C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
C:\Windows\SysWOW64\ntdll.dll (READ)
USER32.dll (EXECUTE)
KERNEL32.dll (EXECUTE)
C:\Windows\Globalization\Sorting\sortdefault.nls (CREATE)
Registry:
HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\C urrentVersion\Setup (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup\SourcePath (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\DevicePath (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings\DisableImprovedZoneCheck (READ)
HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings\Security_HKLM_only (READ)
Process: vscmouse.exe (PID: 3036)
Filesystem:
C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
C:\Windows\SysWOW64\ntdll.dll (READ)
USER32.dll (EXECUTE)
KERNEL32.dll (EXECUTE)
C:\Windows\Globalization\Sorting\sortdefault.nls (CREATE)
C:\ (READ)
C:\Windows\System32\uxtheme.dll (EXECUTE)
dwmapi.dll (EXECUTE)
advapi32.dll (EXECUTE)
shell32.dll (EXECUTE)
C:\Users\comp\AppData\Local\vscmouse\vscmouse.exe (CREATE,READ)
C:\Users\comp\AppData\Local\iproppass\iproppass.exe (DELETE)
crypt32.dll (EXECUTE)
urlmon.dll (EXECUTE)
userenv.dll (EXECUTE)
wininet.dll (EXECUTE)
wtsapi32.dll (EXECUTE)
CRYPTSP.dll (EXECUTE)
CRYPTBASE.dll (EXECUTE)
ole32.dll (EXECUTE)
OLEAUT32.dll (EXECUTE)
C:\Windows\SysWOW64\oleaut32.dll (EXECUTE)
IPHLPAPI.DLL (EXECUTE)
DHCPCSVC.DLL (EXECUTE)
C:\Users\comp\AppData\Roaming\Microsoft\Network\Connections\Pbk\_hiddenPbk\ (CREATE)
C:\Users\comp\AppData\Roaming\Microsoft\Network\Connections\Pbk\_hiddenPbk\rasphone.pbk (CREATE,READ)
Registry:
HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ )
HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup (READ)
[...]
Network:
24.151.31.150:465 (READ)
http://24.151.31.150:465 (READ,WRITE)
107.10.49.252:80 (READ)
http://107.10.49.252:80 (READ,WRITE)
Based on the shown output and the accessed resources, we can deduce some malware features:
601941F00B194587C9E5.exe (PID 1800), the Zone Identifier of the file C:\Users\comp\AppData\Local\vscmouse\vscmouse.exe is deletedvscmouse.exe (PID: 3036) connects to the network endpoints http://24.151.31.150:465 and http://107.10.49.252:80
The accessed resources are interesting for identifying host- and network-based detection indicators. In addition, resources can be used in dynmx signatures. A popular example is the detection of persistence mechanisms in the Registry.
In order to use the software Python 3.9 must be available on the target system. In addition, the following Python packages need to be installed:
anytree,lxml,pyparsing,PyYAML,six andstringcaseTo install the packages run the pip3 command shown below. It is recommended to use a Python virtual environment instead of installing the packages system-wide.
pip3 install -r requirements.txt
To use the prototype, simply run the main entry point dynmx.py. The usage information can be viewed with the -h command line parameter as shown below.
$ python3 dynmx.py -h
usage: dynmx.py [-h] [--format {overview,detail}] [--show-log] [--log LOG] [--log-level {debug,info,error}] [--worker N] {detect,check,convert,stats,resources} ...
Detect dynmx signatures in dynamic program execution information (function logs)
optional arguments:
-h, --help show this help message and exit
--format {overview,detail}, -f {overview,detail}
Output format
--show-log Show all log output on stdout
--log LOG, -l LOG log file
--log-level {debug,info,error}
Log level (default: info)
--worker N, -w N Number of workers to spawn (default: number of processors - 2)
sub-commands:
task to perform
{detect,check,convert,stats,resources}
detect Detects a dynmx signature
check Checks the syntax of dynmx signature(s)
convert Converts function logs to the dynmx generic function log format
stats Statistics of function logs
resources Resource activity derived from function log
In general, as shown in the output, several command line parameters regarding the log handling, the output format for results or multiprocessing can be defined. Furthermore, a command needs be chosen to run a specific task. Please note, that the number of workers only affects commands that make use of multiprocessing. Currently, these are the commands detect and convert.
The commands have specific command line parameters that can be explored by giving the parameter -h to the command, e.g. for the detect command as shown below.
$ python3 dynmx.py detect -h
usage: dynmx.py detect [-h] --sig SIG [SIG ...] --input INPUT [INPUT ...] [--recursive] [--json-result JSON_RESULT] [--runtime-result RUNTIME_RESULT] [--detect-all]
optional arguments:
-h, --help show this help message and exit
--recursive, -r Search for input files recursively
--json-result JSON_RESULT
JSON formatted result file
--runtime-result RUNTIME_RESULT
Runtime statistics file formatted in CSV
--detect-all Detect signature in all processes and do not stop after the first detection
required arguments:
--sig SIG [SIG ...], -s SIG [SIG ...]
dynmx signature(s) to detect
--input INPUT [INPUT ...], -i INPUT [INPUT ...]
Input files
As a user of dynmx, you can decide how the output is structured. If you choose to show the log on the console by defining the parameter --show-log, the output consists of two sections (see listing below). The log is shown first and afterwards the results of the used command. By default, the log is neither shown in the console nor written to a log file (which can be defined using the --log parameter). Due to multiprocessing, the entries in the log file are not necessarily in chronological order.
|
__| _ _ _ _ _
/ | | | / |/ | / |/ |/ | /\/
\_/|_/ \_/|/ | |_/ | | |_/ /\_/
/|
\|
Ver. 0.5 (PoC), by 0x534a
[+] Log output
2023-06-27 19:07:38,068+0000 [INFO] (__main__) [PID: 13315] []: Start of dynmx run
[...]
[+] End of log output
[+] Result
[...]
The level of detail of the result output can be defined using the command line parameter --output-format which can be set to overview for a high-level result or to detail for a detailed result. For example, if you define the output format to detail, detection results shown in the console will contain the exact API calls and resources that caused the detection. The overview output format will just indicate what signature was detected in which function log.
Detection of a dynmx signature in a function log with one worker process
python3 dynmx.py -w 1 detect -i "flog.txt" -s dynmx_signature.yml
Conversion of a function log to the dynmx generic function log format
python3 dynmx.py convert -i "flog.txt" -o /tmp/
Check a signature (only basic sanity checks)
python3 dynmx.py check -s dynmx_signature.yml
Get a detailed list of used resources used by a malware sample based on the function log (access activity model)
python3 dynmx.py -f detail resources -i "flog.txt"
Please consider that this tool is a proof-of-concept which was developed besides writing the master thesis. Hence, the code quality is not always the best and there may be bugs and errors. I tried to make the tool as robust as possible in the given time frame.
The best way to troubleshoot errors is to enable logging (on the console and/or to a log file) and set the log level to debug. Exception handlers should write detailed errors to the log which can help troubleshooting.
A Pin Tool for tracing:
Bypasses the anti-tracing check based on RDTSC.
Generates a report in a .tag format (which can be loaded into other analysis tools):
RVA;traced eventi.e.
345c2;section: .text
58069;called: C:\Windows\SysWOW64\kernel32.dll.IsProcessorFeaturePresent
3976d;called: C:\Windows\SysWOW64\kernel32.dll.LoadLibraryExW
3983c;called: C:\Windows\SysWOW64\kernel32.dll.GetProcAddress
3999d;called: C:\Windows\SysWOW64\KernelBase.dll.InitializeCriticalSectionEx
398ac;called: C:\Windows\SysWOW64\KernelBase.dll.FlsAlloc
3995d;called: C:\Windows\SysWOW64\KernelBase.dll.FlsSetValue
49275;called: C:\Windows\SysWOW64\kernel32.dll.LoadLibraryExW
4934b;called: C:\Windows\SysWOW64\kernel32.dll.GetProcAddress
...To compile the prepared project you need to use Visual Studio >= 2012. It was tested with Intel Pin 3.28.
Clone this repo into \source\tools that is inside your Pin root directory. Open the project in Visual Studio and build. Detailed description available here.
To build with Intel Pin < 3.26 on Windows, use the appropriate legacy Visual Studio project.
For now the support for Linux is experimental. Yet it is possible to build and use Tiny Tracer on Linux as well. Please refer tiny_runner.sh for more information. Detailed description available here.
Details about the usage you will find on the project's Wiki.
install32_64 you can find a utility that checks if Kernel Debugger is disabled (kdb_check.exe, source), and it is used by the Tiny Tracer's .bat scripts. This utilty sometimes gets flagged as a malware by Windows Defender (it is a known false positive). If you encounter this issue, you may need to exclude the installation directory from Windows Defender scans.Questions? Ideas? Join Discussions!
| Language | Framework | URL | Method | Param | Header | WS |
|---|---|---|---|---|---|---|
| Go | Echo | ✅ | ✅ | X | X | X |
| Python | Django | ✅ | X | X | X | X |
| Python | Flask | ✅ | X | X | X | X |
| Ruby | Rails | ✅ | ✅ | ✅ | X | X |
| Ruby | Sinatra | ✅ | ✅ | ✅ | X | X |
| Php | ✅ | ✅ | ✅ | X | X | |
| Java | Spring | ✅ | ✅ | X | X | X |
| Java | Jsp | X | X | X | X | X |
| Crystal | Kemal | ✅ | ✅ | ✅ | X | ✅ |
| JS | Express | ✅ | ✅ | X | X | X |
| JS | Next | X | X | X | X | X |
| Specification | Format | URL | Method | Param | Header | WS |
|---|---|---|---|---|---|---|
| Swagger | JSON | ✅ | ✅ | ✅ | X | X |
| Swagger | YAML | ✅ | ✅ | ✅ | X | X |
brew tap hahwul/noir
brew install noir# Install Crystal-lang
# https://crystal-lang.org/install/
# Clone this repo
git clone https://github.com/hahwul/noir
cd noir
# Install Dependencies
shards install
# Build
shards build --release --no-debug
# Copy binary
cp ./bin/noir /usr/bin/docker pull ghcr.io/hahwul/noir:mainUsage: noir <flags>
Basic:
-b PATH, --base-path ./app (Required) Set base path
-u URL, --url http://.. Set base url for endpoints
-s SCOPE, --scope url,param Set scope for detection
Output:
-f FORMAT, --format json Set output format [plain/json/markdown-table/curl/httpie]
-o PATH, --output out.txt Write result to file
--set-pvalue VALUE Specifies the value of the identified parameter
--no-color Disable color output
--no-log Displaying only the results
Deliver:
--send-req Send the results to the web request
--send-proxy http://proxy.. Send the results to the web request via http proxy
Technologies:
-t TECHS, --techs rails,php Set technologies to use
--exclude-techs rails,php Specify the technologies to be excluded
--list-techs Show all technologies
Others:
-d, --debug Show debug messages
-v, --version Show version
-h, --help Show help
Example
noir -b . -u https://testapp.internal.domainsJSON Result
noir -b . -u https://testapp.internal.domains -f json
[
...
{
"headers": [],
"method": "POST",
"params": [
{
"name": "article_slug",
"param_type": "json",
"value": ""
},
{
"name": "body",
"param_type": "json",
"value": ""
},
{
"name": "id",
"param_type": "json",
"value": ""
}
],
"protocol": "http",
"url": "https://testapp.internal.domains/comments"
}
]
Toolkit demonstrating another approach of a QRLJacking attack, allowing to perform remote account takeover, through sign-in QR code phishing.
It consists of a browser extension used by the attacker to extract the sign-in QR code and a server application, which retrieves the sign-in QR codes to display them on the hosted phishing pages.
Watch the demo video:
Read more about it on my blog: https://breakdev.org/evilqr-phishing
The parameters used by Evil QR are hardcoded into extension and server source code, so it is important to change them to use custom values, before you build and deploy the toolkit.
| parameter | description | default value |
|---|---|---|
| API_TOKEN | API token used to authenticate with REST API endpoints hosted on the server | 00000000-0000-0000-0000-000000000000 |
| QRCODE_ID | QR code ID used to bind the extracted QR code with the one displayed on the phishing page | 11111111-1111-1111-1111-111111111111 |
| BIND_ADDRESS | IP address with port the HTTP server will be listening on | 127.0.0.1:35000 |
| API_URL | External URL pointing to the server, where the phishing page will be hosted | http://127.0.0.1:35000 |
Here are all the places in the source code, where the values should be modified:
You can load the extension in Chrome, through Load unpacked feature: https://developer.chrome.com/docs/extensions/mv3/getstarted/development-basics/#load-unpacked
Once the extension is installed, make sure to pin its icon in Chrome's extension toolbar, so that the icon is always visible.
Make sure you have Go installed version at least 1.20.
To build go to /server directory and run the command:
Windows:
build_run.bat
Linux:
chmod 700 build.sh
./build.sh
Built server binaries will be placed in the ./build/ directory.
./server/build/evilqr-server
https://discord.com/login
https://web.telegram.org/k/
https://whatsapp.com
https://store.steampowered.com/login/
https://accounts.binance.com/en/login
https://www.tiktok.com/login
http://127.0.0.1:35000 (default)Evil QR is made by Kuba Gretzky (@mrgretzky) and it's released under MIT license.
AiCEF is a tool implementing the accompanying framework [1] in order to harness the intelligence that is available from online resources, as well as threat groups' activities, arsenal (eg. MITRE), to create relevant and timely cybersecurity exercise content. This way, we abstract the events from the reports in a machine-readable form. The produced graphs can be infused with additional intelligence, e.g. the threat actor profile from MITRE, also mapped in our ontology. While this may fill gaps that would be missing from a report, one can also manipulate the graph to create custom and unique models. Finally, we exploit transformer-based language models like GPT to convert the graph into text that can serve as the scenario of a cybersecurity exercise. We have tested and validated AiCEF with a group of experts in cybersecurity exercises, and the results clearly show that AiCEF significantly augments the capabilities in creating timely and relevant cybersecurity exercises in terms of both quality and time.
We used Python to create a machine-learning-powered Exercise Generation Framework and developed a set of tools to perform a set of individual tasks which would help an exercise planner (EP) to create a timely and targeted Cybersecurity Exercise Scenario, regardless of her experience.
Problems an Exercise Planner faces:
Our Main Objective: Build an AI powered tool that can generate relevant and up-to-date Cyber Exercise Content in a few steps with little technical expertise from the user.
The updated project, AiCEF v.2.0 is planned to be publicly released by the end of 2023, pending heavy code review and functionality updates. Submodules with reduced functinality will start being release by early June 2023. Thank you for your patience.
The most convenient way to install AiCEF is by using the docker-compose command. For production deployment, we advise you deploy MySQL manually in a dedicated environment and then to start the other components using Docker.
First, make sure you have docker-compose installed in your environment:
$ sudo apt-get install docker-composeThen, clone the repository:
$ git clone https://github.com/grazvan/AiCEF/docker.git /<choose-a-path>/AiCEF-docker
$ cd /<choose-a-path>/AiCEF-dockerImport the MySQL file in your
$ mysql -u <your_username> –-password=<your_password> AiCEF_db < AiCEF_db.sql Before running the docker-compose command, settings must be configured. Copy the sample settings file and change it accordingly to your needs.
$ cp .env.sample .envNote: Make sure you have an OpenAI API key available. Load the environment setttings (including your MySQL connection details):
set -a ; source .envFinally, run docker-compose in detached (-d) mode:
$ sudo docker-compose up -dA common usage flow consists of generating a Trend Report to analyze patterns over time, parsing relevant articles and converting them into Incident Breadcrumbs using MLTP module and storing them in a knowledge database called KDb. Incidents are then generated using IncGen component and can be enhanced using the Graph Enhancer module to simulate known APT activity. The incidents come with injects that can be edited on the fly. The CSE scenario is then created using CEGen, which defines various attributes like CSE name, number of Events, and Incidents. MLCESO is a crucial step in the methodology where dedicated ML models are trained to extract information from the collected articles with over 80% accuracy. The Incident Generation & Enhancer (IncGen) workflow can be automated, generating a variety of incidents based on filtering parameters and the existing database. The knowledge database (KDB) consists of almost 3000 articles classified into six categories that can be augmented using APT Enhancer by using the activity of known APT groups from MITRE or manually.
Find below some sample usage screenshots:
AiCEF is a product designed and developed by Alex Zacharis, Razvan Gavrila and Constantinos Patsakis.
[1] https://link.springer.com/article/10.1007/s10207-023-00693-z
[2] https://oasis-open.github.io/cti-documentation/stix/intro.html
Contributions are welcome! If you'd like to contribute to AiCEF v2.0, please follow these steps:
git checkout -b feature/your-branch-name)git commit -m 'Add some feature')git push origin feature/your-branch-name)AiCEF is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. See for more information.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
The VX-API is a collection of malicious functionality to aid in malware development. It is recommended you clone and/or download this entire repo then open the Visual Studio solution file to easily explore functionality and concepts.
Some functions may be dependent on other functions present within the solution file. Using the solution file provided here will make it easier to identify which other functionality and/or header data is required.
You're free to use this in any manner you please. You do not need to use this entire solution for your malware proof-of-concepts or Red Team engagements. Strip, copy, paste, delete, or edit this projects contents as much as you'd like.
| Function Name | Original Author |
|---|---|
| AdfCloseHandleOnInvalidAddress | Checkpoint Research |
| AdfIsCreateProcessDebugEventCodeSet | Checkpoint Research |
| AdfOpenProcessOnCsrss | Checkpoint Research |
| CheckRemoteDebuggerPresent2 | ReactOS |
| IsDebuggerPresentEx | smelly__vx |
| IsIntelHardwareBreakpointPresent | Checkpoint Research |
| Function Name | Original Author |
|---|---|
| HashStringDjb2 | Dan Bernstein |
| HashStringFowlerNollVoVariant1a | Glenn Fowler, Landon Curt Noll, and Kiem-Phong Vo |
| HashStringJenkinsOneAtATime32Bit | Bob Jenkins |
| HashStringLoseLose | Brian Kernighan and Dennis Ritchie |
| HashStringRotr32 | T. Oshiba (1972) |
| HashStringSdbm | Ozan Yigit |
| HashStringSuperFastHash | Paul Hsieh |
| HashStringUnknownGenericHash1A | Unknown |
| HashStringSipHash | RistBS |
| HashStringMurmur | RistBS |
| CreateMd5HashFromFilePath | Microsoft |
| CreatePseudoRandomInteger | Apple (c) 1999 |
| CreatePseudoRandomString | smelly__vx |
| HashFileByMsiFileHashTable | smelly__vx |
| CreatePseudoRandomIntegerFromNtdll | smelly__vx |
| LzMaximumCompressBuffer | smelly__vx |
| LzMaximumDecompressBuffer | smelly__vx |
| LzStandardCompressBuffer | smelly__vx |
| LzStandardDecompressBuffer | smelly__vx |
| XpressHuffMaximumCompressBuffer | smelly__vx |
| XpressHuffMaximumDecompressBuffer | smelly__vx |
| XpressHuffStandardCompressBuffer | smelly__vx |
| XpressHuffStandardDecompressBuffer | smelly__vx |
| XpressMaximumCompressBuffer | smelly__vx |
| XpressMaximumDecompressBuffer | smelly__vx |
| XpressStandardCompressBuffer | smelly__vx |
| XpressStandardDecompressBuffer | smelly__vx |
| ExtractFilesFromCabIntoTarget | smelly__vx |
| Function Name | Original Author |
|---|---|
| GetLastErrorFromTeb | smelly__vx |
| GetLastNtStatusFromTeb | smelly__vx |
| RtlNtStatusToDosErrorViaImport | ReactOS |
| GetLastErrorFromTeb | smelly__vx |
| SetLastErrorInTeb | smelly__vx |
| SetLastNtStatusInTeb | smelly__vx |
| Win32FromHResult | Raymond Chen |
| Function Name | Original Author |
|---|---|
| AmsiBypassViaPatternScan | ZeroMemoryEx |
| DelayedExecutionExecuteOnDisplayOff | am0nsec and smelly__vx |
| HookEngineRestoreHeapFree | rad9800 |
| MasqueradePebAsExplorer | smelly__vx |
| RemoveDllFromPeb | rad9800 |
| RemoveRegisterDllNotification | Rad98, Peter Winter-Smith |
| SleepObfuscationViaVirtualProtect | 5pider |
| RtlSetBaseUnicodeCommandLine | TheWover |
| Function Name | Original Author |
|---|---|
| GetCurrentLocaleFromTeb | 3xp0rt |
| GetNumberOfLinkedDlls | smelly__vx |
| GetOsBuildNumberFromPeb | smelly__vx |
| GetOsMajorVersionFromPeb | smelly__vx |
| GetOsMinorVersionFromPeb | smelly__vx |
| GetOsPlatformIdFromPeb | smelly__vx |
| IsNvidiaGraphicsCardPresent | smelly__vx |
| IsProcessRunning | smelly__vx |
| IsProcessRunningAsAdmin | Vimal Shekar |
| GetPidFromNtQuerySystemInformation | smelly__vx |
| GetPidFromWindowsTerminalService | modexp |
| GetPidFromWmiComInterface | aalimian and modexp |
| GetPidFromEnumProcesses | smelly__vx |
| GetPidFromPidBruteForcing | modexp |
| GetPidFromNtQueryFileInformation | modexp, Lloyd Davies, Jonas Lyk |
| GetPidFromPidBruteForcingExW | smelly__vx, LLoyd Davies, Jonas Lyk, modexp |
| Function Name | Original Author |
|---|---|
| CreateLocalAppDataObjectPath | smelly__vx |
| CreateWindowsObjectPath | smelly__vx |
| GetCurrentDirectoryFromUserProcessParameters | smelly__vx |
| GetCurrentProcessIdFromTeb | ReactOS |
| GetCurrentUserSid | Giovanni Dicanio |
| GetCurrentWindowTextFromUserProcessParameter | smelly__vx |
| GetFileSizeFromPath | smelly__vx |
| GetProcessHeapFromTeb | smelly__vx |
| GetProcessPathFromLoaderLoadModule | smelly__vx |
| GetProcessPathFromUserProcessParameters | smelly__vx |
| GetSystemWindowsDirectory | Geoff Chappell |
| IsPathValid | smelly__vx |
| RecursiveFindFile | Luke |
| SetProcessPrivilegeToken | Microsoft |
| IsDllLoaded | smelly__vx |
| TryLoadDllMultiMethod | smelly__vx |
| CreateThreadAndWaitForCompletion | smelly__vx |
| GetProcessBinaryNameFromHwndW | smelly__vx |
| GetByteArrayFromFile | smelly__vx |
| Ex_GetHandleOnDeviceHttpCommunication | x86matthew |
| IsRegistryKeyValid | smelly__vx |
| FastcallExecuteBinaryShellExecuteEx | smelly__vx |
| GetCurrentProcessIdFromOffset | RistBS |
| GetPeBaseAddress | smelly__vx |
| LdrLoadGetProcedureAddress | c5pider |
| IsPeSection | smelly__vx |
| AddSectionToPeFile | smelly__vx |
| WriteDataToPeSection | smelly__vx |
| GetPeSectionSizeInByte | smelly__vx |
| ReadDataFromPeSection | smelly__vx |
| GetCurrentProcessNoForward | ReactOS |
| GetCurrentThreadNoForward | ReactOS |
| Function Name | Original Author |
|---|---|
| GetKUserSharedData | Geoff Chappell |
| GetModuleHandleEx2 | smelly__vx |
| GetPeb | 29a |
| GetPebFromTeb | ReactOS |
| GetProcAddress | 29a Volume 2, c5pider |
| GetProcAddressDjb2 | smelly__vx |
| GetProcAddressFowlerNollVoVariant1a | smelly__vx |
| GetProcAddressJenkinsOneAtATime32Bit | smelly__vx |
| GetProcAddressLoseLose | smelly__vx |
| GetProcAddressRotr32 | smelly__vx |
| GetProcAddressSdbm | smelly__vx |
| GetProcAddressSuperFastHash | smelly__vx |
| GetProcAddressUnknownGenericHash1 | smelly__vx |
| GetProcAddressSipHash | RistBS |
| GetProcAddressMurmur | RistBS |
| GetRtlUserProcessParameters | ReactOS |
| GetTeb | ReactOS |
| RtlLoadPeHeaders | smelly__vx |
| ProxyWorkItemLoadLibrary | Rad98, Peter Winter-Smith |
| ProxyRegisterWaitLoadLibrary | Rad98, Peter Winter-Smith |
| Function Name | Original Author |
|---|---|
| MpfGetLsaPidFromServiceManager | modexp |
| MpfGetLsaPidFromRegistry | modexp |
| MpfGetLsaPidFromNamedPipe | modexp |
| Function Name | Original Author |
|---|---|
| UrlDownloadToFileSynchronous | Hans Passant |
| ConvertIPv4IpAddressStructureToString | smelly__vx |
| ConvertIPv4StringToUnsignedLong | smelly__vx |
| SendIcmpEchoMessageToIPv4Host | smelly__vx |
| ConvertIPv4IpAddressUnsignedLongToString | smelly__vx |
| DnsGetDomainNameIPv4AddressAsString | smelly__vx |
| DnsGetDomainNameIPv4AddressUnsignedLong | smelly__vx |
| GetDomainNameFromUnsignedLongIPV4Address | smelly__vx |
| GetDomainNameFromIPV4AddressAsString | smelly__vx |
| Function Name | Original Author |
|---|---|
| OleGetClipboardData | Microsoft |
| MpfComVssDeleteShadowVolumeBackups | am0nsec |
| MpfComModifyShortcutTarget | Unknown |
| MpfComMonitorChromeSessionOnce | smelly__vx |
| MpfExtractMaliciousPayloadFromZipFileNoPassword | Codu |
| Function Name | Original Author |
|---|---|
| CreateProcessFromIHxHelpPaneServer | James Forshaw |
| CreateProcessFromIHxInteractiveUser | James Forshaw |
| CreateProcessFromIShellDispatchInvoke | Mohamed Fakroud |
| CreateProcessFromShellExecuteInExplorerProcess | Microsoft |
| CreateProcessViaNtCreateUserProcess | CaptMeelo |
| CreateProcessWithCfGuard | smelly__vx and Adam Chester |
| CreateProcessByWindowsRHotKey | smelly__vx |
| CreateProcessByWindowsRHotKeyEx | smelly__vx |
| CreateProcessFromINFSectionInstallStringNoCab | smelly__vx |
| CreateProcessFromINFSetupCommand | smelly__vx |
| CreateProcessFromINFSectionInstallStringNoCab2 | smelly__vx |
| CreateProcessFromIeFrameOpenUrl | smelly__vx |
| CreateProcessFromPcwUtil | smelly__vx |
| CreateProcessFromShdocVwOpenUrl | smelly__vx |
| CreateProcessFromShell32ShellExecRun | smelly__vx |
| MpfExecute64bitPeBinaryInMemoryFromByteArrayNoReloc | aaaddress1 |
| CreateProcessFromWmiWin32_ProcessW | CIA |
| CreateProcessFromZipfldrRouteCall | smelly__vx |
| CreateProcessFromUrlFileProtocolHandler | smelly__vx |
| CreateProcessFromUrlOpenUrl | smelly__vx |
| CreateProcessFromMsHTMLW | smelly__vx |
| Function Name | Original Author |
|---|---|
| MpfPiControlInjection | SafeBreach Labs |
| MpfPiQueueUserAPCViaAtomBomb | SafeBreach Labs |
| MpfPiWriteProcessMemoryCreateRemoteThread | SafeBreach Labs |
| MpfProcessInjectionViaProcessReflection | Deep Instinct |
| Function Name | Original Author |
|---|---|
| IeCreateFile | smelly__vx |
| CopyFileViaSetupCopyFile | smelly__vx |
| CreateFileFromDsCopyFromSharedFile | Jonas Lyk |
| DeleteDirectoryAndSubDataViaDelNode | smelly__vx |
| DeleteFileWithCreateFileFlag | smelly__vx |
| IsProcessRunningAsAdmin2 | smelly__vx |
| IeCreateDirectory | smelly__vx |
| IeDeleteFile | smelly__vx |
| IeFindFirstFile | smelly__vx |
| IEGetFileAttributesEx | smelly__vx |
| IeMoveFileEx | smelly__vx |
| IeRemoveDirectory | smelly__vx |
| Function Name | Original Author |
|---|---|
| MpfSceViaImmEnumInputContext | alfarom256, aahmad097 |
| MpfSceViaCertFindChainInStore | alfarom256, aahmad097 |
| MpfSceViaEnumPropsExW | alfarom256, aahmad097 |
| MpfSceViaCreateThreadpoolWait | alfarom256, aahmad097 |
| MpfSceViaCryptEnumOIDInfo | alfarom256, aahmad097 |
| MpfSceViaDSA_EnumCallback | alfarom256, aahmad097 |
| MpfSceViaCreateTimerQueueTimer | alfarom256, aahmad097 |
| MpfSceViaEvtSubscribe | alfarom256, aahmad097 |
| MpfSceViaFlsAlloc | alfarom256, aahmad097 |
| MpfSceViaInitOnceExecuteOnce | alfarom256, aahmad097 |
| MpfSceViaEnumChildWindows | alfarom256, aahmad097, wra7h |
| MpfSceViaCDefFolderMenu_Create2 | alfarom256, aahmad097, wra7h |
| MpfSceViaCertEnumSystemStore | alfarom256, aahmad097, wra7h |
| MpfSceViaCertEnumSystemStoreLocation | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumDateFormatsW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumDesktopWindows | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumDesktopsW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumDirTreeW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumDisplayMonitors | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumFontFamiliesExW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumFontsW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumLanguageGroupLocalesW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumObjects | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumResourceTypesExW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumSystemCodePagesW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumSystemGeoID | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumSystemLanguageGroupsW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumSystemLocalesEx | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumThreadWindows | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumTimeFormatsEx | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumUILanguagesW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumWindowStationsW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumWindows | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumerateLoadedModules64 | alfarom256, aahmad097, wra7h |
| MpfSceViaK32EnumPageFilesW | alfarom256, aahmad097, wra7h |
| MpfSceViaEnumPwrSchemes | alfarom256, aahmad097, wra7h |
| MpfSceViaMessageBoxIndirectW | alfarom256, aahmad097, wra7h |
| MpfSceViaChooseColorW | alfarom256, aahmad097, wra7h |
| MpfSceViaClusWorkerCreate | alfarom256, aahmad097, wra7h |
| MpfSceViaSymEnumProcesses | alfarom256, aahmad097, wra7h |
| MpfSceViaImageGetDigestStream | alfarom256, aahmad097, wra7h |
| MpfSceViaVerifierEnumerateResource | alfarom256, aahmad097, wra7h |
| MpfSceViaSymEnumSourceFiles | alfarom256, aahmad097, wra7h |
| Function Name | Original Author |
|---|---|
| ByteArrayToCharArray | smelly__vx |
| CharArrayToByteArray | smelly__vx |
| ShlwapiCharStringToWCharString | smelly__vx |
| ShlwapiWCharStringToCharString | smelly__vx |
| CharStringToWCharString | smelly__vx |
| WCharStringToCharString | smelly__vx |
| RtlInitEmptyUnicodeString | ReactOS |
| RtlInitUnicodeString | ReactOS |
| CaplockString | simonc |
| CopyMemoryEx | ReactOS |
| SecureStringCopy | Apple (c) 1999 |
| StringCompare | Apple (c) 1999 |
| StringConcat | Apple (c) 1999 |
| StringCopy | Apple (c) 1999 |
| StringFindSubstring | Apple (c) 1999 |
| StringLength | Apple (c) 1999 |
| StringLocateChar | Apple (c) 1999 |
| StringRemoveSubstring | smelly__vx |
| StringTerminateStringAtChar | smelly__vx |
| StringToken | Apple (c) 1999 |
| ZeroMemoryEx | ReactOS |
| ConvertCharacterStringToIntegerUsingNtdll | smelly__vx |
| MemoryFindMemory | KamilCuk |
| Function Name | Original Author |
|---|---|
| UacBypassFodHelperMethod | winscripting.blog |
| Function Name | Original Author |
|---|---|
| InitHardwareBreakpointEngine | rad98 |
| ShutdownHardwareBreakpointEngine | rad98 |
| ExceptionHandlerCallbackRoutine | rad98 |
| SetHardwareBreakpoint | rad98 |
| InsertDescriptorEntry | rad98 |
| RemoveDescriptorEntry | rad98 |
| SnapshotInsertHardwareBreakpointHookIntoTargetThread | rad98 |
| Function Name | Original Author |
|---|---|
| GenericShellcodeHelloWorldMessageBoxA | SafeBreach Labs |
| GenericShellcodeHelloWorldMessageBoxAEbFbLoop | SafeBreach Labs |
| GenericShellcodeOpenCalcExitThread | MsfVenom |
ReconAIzer is a powerful Jython extension for Burp Suite that leverages OpenAI to help bug bounty hunters optimize their recon process. This extension automates various tasks, making it easier and faster for security researchers to identify and exploit vulnerabilities.
Once installed, ReconAIzer add a contextual menu and a dedicated tab to see the results:
Follow these steps to install the ReconAIzer extension on Burp Suite:
ReconAIzer.py file in Step 3.1. Select the file and click "Open."Congratulations! You have successfully installed the ReconAIzer extension in Burp Suite. You can now start using it to enhance your bug bounty hunting experience.
Once it's done, you must configure your OpenAI API key on the "Config" tab under "ReconAIzer" tab.
Feel free to suggest prompts improvements or anything you would like to see on ReconAIzer!
Happy bug hunting!
HardHat is a multiplayer C# .NET-based command and control framework. Designed to aid in red team engagements and penetration testing. HardHat aims to improve the quality of life factors during engagements by providing an easy-to-use but still robust C2 framework.
It contains three primary components, an ASP.NET teamserver, a blazor .NET client, and C# based implants.
Alpha Release - 3/29/23 NOTE: HardHat is in Alpha release; it will have bugs, missing features, and unexpected things will happen. Thank you for trying it, and please report back any issues or missing features so they can be addressed.
Discord Join the community to talk about HardHat C2, Programming, Red teaming and general cyber security things The discord community is also a great way to request help, submit new features, stay up to date on the latest additions, and submit bugs.
documentation can be found at docs
To configure the team server's starting address (where clients will connect), edit the HardHatC2\TeamServer\Properties\LaunchSettings.json changing the "applicationUrl": "https://127.0.0.1:5000" to the desired location and port. start the teamserver with dotnet run from its top-level folder ../HrdHatC2/Teamserver/
Code contributions are welcome feel free to submit feature requests, pull requests or send me your ideas on discord.
burpgpt leverages the power of AI to detect security vulnerabilities that traditional scanners might miss. It sends web traffic to an OpenAI model specified by the user, enabling sophisticated analysis within the passive scanner. This extension offers customisable prompts that enable tailored web traffic analysis to meet the specific needs of each user. Check out the Example Use Cases section for inspiration.
The extension generates an automated security report that summarises potential security issues based on the user's prompt and real-time data from Burp-issued requests. By leveraging AI and natural language processing, the extension streamlines the security assessment process and provides security professionals with a higher-level overview of the scanned application or endpoint. This enables them to more easily identify potential security issues and prioritise their analysis, while also covering a larger potential attack surface.
[!WARNING] Data traffic is sent to
OpenAIfor analysis. If you have concerns about this or are using the extension for security-critical applications, it is important to carefully consider this and review OpenAI's Privacy Policy for further information.
[!WARNING] While the report is automated, it still requires triaging and post-processing by security professionals, as it may contain false positives.
[!WARNING] The effectiveness of this extension is heavily reliant on the quality and precision of the prompts created by the user for the selected
GPTmodel. This targeted approach will help ensure theGPT modelgenerates accurate and valuable results for your security analysis.
passive scan check, allowing users to submit HTTP data to an OpenAI-controlled GPT model for analysis through a placeholder system.OpenAI's GPT models to conduct comprehensive traffic analysis, enabling detection of various issues beyond just security vulnerabilities in scanned applications.GPT tokens used in the analysis by allowing for precise adjustments of the maximum prompt length.OpenAI models to choose from, allowing them to select the one that best suits their needs.prompts and unleash limitless possibilities for interacting with OpenAI models. Browse through the Example Use Cases for inspiration.Burp Suite, providing all native features for pre- and post-processing, including displaying analysis results directly within the Burp UI for efficient analysis.Burp Event Log, enabling users to quickly resolve communication issues with the OpenAI API.Operating System: Compatible with Linux, macOS, and Windows operating systems.
Java Development Kit (JDK): Version 11 or later.
Burp Suite Professional or Community Edition: Version 2023.3.2 or later.
[!IMPORTANT] Please note that using any version lower than
2023.3.2may result in a java.lang.NoSuchMethodError. It is crucial to use the specified version or a more recent one to avoid this issue.
Version 6.9 or later (recommended). The build.gradle file is provided in the project repository.JAVA_HOME environment variable to point to the JDK installation directory.Please ensure that all system requirements, including a compatible version of Burp Suite, are met before building and running the project. Note that the project's external dependencies will be automatically managed and installed by Gradle during the build process. Adhering to the requirements will help avoid potential issues and reduce the need for opening new issues in the project repository.
Ensure you have Gradle installed and configured.
Download the burpgpt repository:
git clone https://github.com/aress31/burpgpt
cd .\burpgpt\Build the standalone jar:
./gradlew shadowJarBurp Suite
To install burpgpt in Burp Suite, first go to the Extensions tab and click on the Add button. Then, select the burpgpt-all jar file located in the .\lib\build\libs folder to load the extension.
To start using burpgpt, users need to complete the following steps in the Settings panel, which can be accessed from the Burp Suite menu bar:
OpenAI API key.model.max prompt size. This field controls the maximum prompt length sent to OpenAI to avoid exceeding the maxTokens of GPT models (typically around 2048 for GPT-3).Once configured as outlined above, the Burp passive scanner sends each request to the chosen OpenAI model via the OpenAI API for analysis, producing Informational-level severity findings based on the results.
burpgpt enables users to tailor the prompt for traffic analysis using a placeholder system. To include relevant information, we recommend using these placeholders, which the extension handles directly, allowing dynamic insertion of specific values into the prompt:
| Placeholder | Description |
|---|---|
{REQUEST} | The scanned request. |
{URL} | The URL of the scanned request. |
{METHOD} | The HTTP request method used in the scanned request. |
{REQUEST_HEADERS} | The headers of the scanned request. |
{REQUEST_BODY} | The body of the scanned request. |
{RESPONSE} | The scanned response. |
{RESPONSE_HEADERS} | The headers of the scanned response. |
{RESPONSE_BODY} | The body of the scanned response. |
{IS_TRUNCATED_PROMPT} | A boolean value that is programmatically set to true or false to indicate whether the prompt was truncated to the Maximum Prompt Size defined in the Settings. |
These placeholders can be used in the custom prompt to dynamically generate a request/response analysis prompt that is specific to the scanned request.
[!NOTE] >
Burp Suiteprovides the capability to support arbitraryplaceholdersthrough the use of Session handling rules or extensions such as Custom Parameter Handler, allowing for even greater customisation of theprompts.
The following list of example use cases showcases the bespoke and highly customisable nature of burpgpt, which enables users to tailor their web traffic analysis to meet their specific needs.
Identifying potential vulnerabilities in web applications that use a crypto library affected by a specific CVE:
Analyse the request and response data for potential security vulnerabilities related to the {CRYPTO_LIBRARY_NAME} crypto library affected by CVE-{CVE_NUMBER}:
Web Application URL: {URL}
Crypto Library Name: {CRYPTO_LIBRARY_NAME}
CVE Number: CVE-{CVE_NUMBER}
Request Headers: {REQUEST_HEADERS}
Response Headers: {RESPONSE_HEADERS}
Request Body: {REQUEST_BODY}
Response Body: {RESPONSE_BODY}
Identify any potential vulnerabilities related to the {CRYPTO_LIBRARY_NAME} crypto library affected by CVE-{CVE_NUMBER} in the request and response data and report them.
Scanning for vulnerabilities in web applications that use biometric authentication by analysing request and response data related to the authentication process:
Analyse the request and response data for potential security vulnerabilities related to the biometric authentication process:
Web Application URL: {URL}
Biometric Authentication Request Headers: {REQUEST_HEADERS}
Biometric Authentication Response Headers: {RESPONSE_HEADERS}
Biometric Authentication Request Body: {REQUEST_BODY}
Biometric Authentication Response Body: {RESPONSE_BODY}
Identify any potential vulnerabilities related to the biometric authentication process in the request and response data and report them.
Analysing the request and response data exchanged between serverless functions for potential security vulnerabilities:
Analyse the request and response data exchanged between serverless functions for potential security vulnerabilities:
Serverless Function A URL: {URL}
Serverless Function B URL: {URL}
Serverless Function A Request Headers: {REQUEST_HEADERS}
Serverless Function B Response Headers: {RESPONSE_HEADERS}
Serverless Function A Request Body: {REQUEST_BODY}
Serverless Function B Response Body: {RESPONSE_BODY}
Identify any potential vulnerabilities in the data exchanged between the two serverless functions and report them.
Analysing the request and response data for potential security vulnerabilities specific to a Single-Page Application (SPA) framework:
Analyse the request and response data for potential security vulnerabilities specific to the {SPA_FRAMEWORK_NAME} SPA framework:
Web Application URL: {URL}
SPA Framework Name: {SPA_FRAMEWORK_NAME}
Request Headers: {REQUEST_HEADERS}
Response Headers: {RESPONSE_HEADERS}
Request Body: {REQUEST_BODY}
Response Body: {RESPONSE_BODY}
Identify any potential vulnerabilities related to the {SPA_FRAMEWORK_NAME} SPA framework in the request and response data and report them.
Settings panel that allows users to set the maxTokens limit for requests, thereby limiting the request size.AI model, allowing users to run and interact with the model on their local machines, potentially improving response times and data privacy.maxTokens value for each model to transmit the maximum allowable data and obtain the most extensive GPT response possible.Burp Suite restarts.GPT responses into the Vulnerability model for improved reporting.The extension is currently under development and we welcome feedback, comments, and contributions to make it even better.
If this extension has saved you time and hassle during a security assessment, consider showing some love by sponsoring a cup of coffee
for the developer. It's the fuel that powers development, after all. Just hit that shiny Sponsor button at the top of the page or click here to contribute and keep the caffeine flowing.Did you find a bug? Well, don't just let it crawl around! Let's squash it together like a couple of bug whisperers!
Please report any issues on the GitHub issues tracker. Together, we'll make this extension as reliable as a cockroach surviving a nuclear apocalypse!
Looking to make a splash with your mad coding skills?
Awesome! Contributions are welcome and greatly appreciated. Please submit all PRs on the GitHub pull requests tracker. Together we can make this extension even more amazing!
See LICENSE.
Sandboxes are commonly used to analyze malware. They provide a temporary, isolated, and secure environment in which to observe whether a suspicious file exhibits any malicious behavior. However, malware developers have also developed methods to evade sandboxes and analysis environments. One such method is to perform checks to determine whether the machine the malware is being executed on is being operated by a real user. One such check is the RAM size. If the RAM size is unrealistically small (e.g., 1GB), it may indicate that the machine is a sandbox. If the malware detects a sandbox, it will not execute its true malicious behavior and may appear to be a benign file
The GetPhysicallyInstalledSystemMemory API retrieves the amount of RAM that is physically installed on the computer from the SMBIOS firmware tables. It takes a PULONGLONG parameter and returns TRUE if the function succeeds, setting the TotalMemoryInKilobytes to a nonzero value. If the function fails, it returns FALSE.
The amount of physical memory retrieved by the GetPhysicallyInstalledSystemMemory function must be equal to or greater than the amount reported by the GlobalMemoryStatusEx function; if it is less, the SMBIOS data is malformed and the function fails with ERROR_INVALID_DATA, Malformed SMBIOS data may indicate a problem with the user's computer .
The register rcx holds the parameter TotalMemoryInKilobytes. To overwrite the jump address of GetPhysicallyInstalledSystemMemory, I use the following opcodes: mov qword ptr ss:[rcx],4193B840. This moves the value 4193B840 (or 1.1 TB) to rcx. Then, the ret instruction is used to pop the return address off the stack and jump to it, Therefore, whenever GetPhysicallyInstalledSystemMemory is called, it will set rcx to the custom value."
Hades is a proof of concept loader that combines several evasion technques with the aim of bypassing the defensive mechanisms commonly used by modern AV/EDRs.
The easiest way, is probably building the project on Linux using make.
git clone https://github.com/f1zm0/hades && cd hades
makeThen you can bring the executable to a x64 Windows host and run it with .\hades.exe [options].
PS > .\hades.exe -h
'||' '||' | '||''|. '||''''| .|'''.|
|| || ||| || || || . ||.. '
||''''|| | || || || ||''| ''|||.
|| || .''''|. || || || . '||
.||. .||. .|. .||. .||...|' .||.....| |'....|'
version: dev [11/01/23] :: @f1zm0
Usage:
hades -f <filepath> [-t selfthread|remotethread|queueuserapc]
Options:
-f, --file <str> shellcode file path (.bin)
-t, --technique <str> injection technique [selfthread, remotethread, queueuserapc]
Inject shellcode that spawms calc.exe with queueuserapc technique:
.\hades.exe -f calc.bin -t queueuserapc
User-mode hooking bypass with syscall RVA sorting (NtQueueApcThread hooked with frida-trace and custom handler)
Instrumentation callback bypass with indirect syscalls (injected DLL is from syscall-detect by jackullrich)
In the latest release, direct syscall capabilities have been replaced by indirect syscalls provided by acheron. If for some reason you want to use the previous version of the loader that used direct syscalls, you need to explicitly pass the direct_syscalls tag to the compiler, which will figure out what files needs to be included and excluded from the build.
GOOS=windows GOARCH=amd64 go build -ldflags "-s -w" -tags='direct_syscalls' -o dist/hades_directsys.exe cmd/hades/main.go
Warning
This project has been created for educational purposes only, to experiment with malware dev in Go, and learn more about the unsafe package and the weird Go Assembly syntax. Don't use it to on systems you don't own. The developer of this project is not responsible for any damage caused by the improper use of this tool.
Shoutout to the following people that shared their knowledge and code that inspired this tool:
This project is licensed under the GPLv3 License - see the LICENSE file for details
Secure Your API.
With Metlo you can:
Metlo does this by scanning your API traffic using one of our connectors and then analyzing trace data.
There are three ways to get started with Metlo. Metlo Cloud, Metlo Self Hosted, and our Open Source product. We recommend Metlo Cloud for almost all users as it scales to 100s of millions of requests per month and all upgrades and migrations are managed for you.
You can get started with Melto Cloud right away without a credit card. Just make an account on https://app.metlo.com and follow the instructions in our docs here.
Although we highly recommend Metlo Cloud, if you're a large company or need an air-gapped system you can self host Metlo as well! Create an account on https://my.metlo.com and follow the instructions on our docs here to setup Metlo in your own Cloud environment.
If you want to deploy our Open Source product we have instructions for AWS, GCP, Azure and Docker.
You can also join our Discord community if you need help or just want to chat!
For tests that we can't autogenerate, our built in testing framework helps you get to 100% Security Coverage on your highest risk APIs. You can build tests in a yaml format to make sure your API is working as intendend.
For example the following test checks for broken authentication:
id: test-payment-processor-metlo.com-user-billing
meta:
name: test-payment-processor.metlo.com/user/billing Test Auth
severity: CRITICAL
tags:
- BROKEN_AUTHENTICATION
test:
- request:
method: POST
url: https://test-payment-processor.metlo.com/user/billing
headers:
- name: Content-Type
value: application/json
- name: Authorization
value: ...
data: |-
{ "ccn": "...", "cc_exp": "...", "cc_code": "..." }
assert:
- key: resp.status
value: 200
- request:
method: POST
url: https://test-payment-processor.metlo.com/user/billing
headers:
- name: Content-Type
value: application/json
data: |-
{ "ccn": "...", "cc_exp": "...", "cc_code": "..." }
assert:
- key: resp.s tatus
value: [ 401, 403 ]You can see more information on our docs.
Most businesses have adopted public facing APIs to power their websites and apps. This has dramatically increased the attack surface for your business. There’s been a 200% increase in API security breaches in just the last year with the APIs of companies like Uber, Meta, Experian and Just Dial leaking millions of records. It's obvious that tools are needed to help security teams make APIs more secure but there's no great solution on the market.
Some solutions require you to go through sales calls to even try the product while others have you to send all your API traffic to their own cloud. Metlo is the first Open Source API security platform that you can self host, and get started for free right away!
We would love for you to come help us make Metlo better. Come join us at Metlo!
This repo is entirely MIT licensed. Features like user management, user roles and attack protection require an enterprise license. Contact us for more information.
Checkout our development guide for more info on how to develop Metlo locally.
Uses python3.10, Debian, python-Nmap, and flask framework to create a Nmap API that can do scans with a good speed online and is easy to deploy.
This is a implementation for our college PCL project which is still under development and constantly updating.
GET /api/p1/{username}:{password}/{target}
GET /api/p2/{username}:{password}/{target}
GET /api/p3/{username}:{password}/{target}
GET /api/p4/{username}:{password}/{target}
GET /api/p5/{username}:{password}/{target}| Parameter | Type | Description |
|---|---|---|
username | string | Required. username of the current user |
password | string | Required. current user password |
target | string | Required. The target Hostname and IP |
GET /api/p1/
GET /api/p2/
GET /api/p3/
GET /api/p4/
GET /api/p5/| Parameter | Return data | Description | Nmap Command |
|---|---|---|---|
p1 | json | Effective Scan | -Pn -sV -T4 -O -F |
p2 | json | Simple Scan | -Pn -T4 -A -v |
p3 | json | Low Power Scan | -Pn -sS -sU -T4 -A -v |
p4 | json | Partial Intense Scan | -Pn -p- -T4 -A -v |
p5 | json | Complete Intense Scan | -Pn -sS -sU -T4 -A -PE -PP -PS80,443 -PA3389 -PU40125 -PY -g 53 --script=vuln |
POST /adduser/{admin-username}:{admin-passwd}/{id}/{username}/{passwd}
POST /deluser/{admin-username}:{admin-passwd}/{t-username}/{t-userpass}
POST /altusername/{admin-username}:{admin-passwd}/{t-user-id}/{new-t-username}
POST /altuserid/{admin-username}:{admin-passwd}/{new-t-user-id}/{t-username}
POST /altpassword/{admin-username}:{admin-passwd}/{t-username}/{new-t-userpass}| Parameter | Type | Description |
|---|---|---|
admin-username | String | Admin username |
admin-passwd | String | Admin password |
id | String | Id for newly added user |
username | String | Username of the newly added user |
passwd | String | Password of the newly added user |
t-username | String | Target username |
t-user-id | String | Target userID |
t-userpass | String | Target users password |
new-t-username | String | New username for the target |
new-t-user-id | String | New userID for the target |
new-t-userpass | String | New password for the target |
DEFAULT CREDENTIALS
ADMINISTRATOR : zAp6_oO~t428)@,
WAF bypass Tool is an open source tool to analyze the security of any WAF for False Positives and False Negatives using predefined and customizable payloads. Check your WAF before an attacker does. WAF Bypass Tool is developed by Nemesida WAF team with the participation of community.
It is forbidden to use for illegal and illegal purposes. Don't break the law. We are not responsible for possible risks associated with the use of this software.
The latest waf-bypass always available via the Docker Hub. It can be easily pulled via the following command:
# docker pull nemesida/waf-bypass
# docker run nemesida/waf-bypass --host='example.com'
# git clone https://github.com/nemesida-waf/waf_bypass.git /opt/waf-bypass/
# python3 -m pip install -r /opt/waf-bypass/requirements.txt
# python3 /opt/waf-bypass/main.py --host='example.com'
'--proxy' (--proxy='http://proxy.example.com:3128') - option allows to specify where to connect to instead of the host.
'--header' (--header 'Authorization: Basic YWRtaW46YWRtaW4=' --header 'X-TOKEN: ABCDEF') - option allows to specify the HTTP header to send with all requests (e.g. for authentication). Multiple use is allowed.
'--user-agent' (--user-agent 'MyUserAgent 1/1') - option allows to specify the HTTP User-Agent to send with all requests, except when the User-Agent is set by the payload ("USER-AGENT").
'--block-code' (--block-code='403' --block-code='222') - option allows you to specify the HTTP status code to expect when the WAF is blocked. (default is 403). Multiple use is allowed.
'--threads' (--threads=15) - option allows to specify the number of parallel scan threads (default is 10).
'--timeout' (--timeout=10) - option allows to specify a request processing timeout in sec. (default is 30).
'--json-format' - an option that allows you to display the result of the work in JSON format (useful for integrating the tool with security platforms).
'--details' - display the False Positive and False Negative payloads. Not available in JSON format.
'--exclude-dir' - exclude the payload's directory (--exclude-dir='SQLi' --exclude-dir='XSS'). Multiple use is allowed.
Depending on the purpose, payloads are located in the appropriate folders:
When compiling a payload, the following zones, method and options are used:
Base64, HTML-ENTITY, UTF-16) in addition to the encoding for the payload. Multiple values are indicated with a space (e.g. Base64 UTF-16). Applicable only to for ARGS, BODY, COOKIE and HEADER zone. Not applicable to payloads in API and MFD directories. Not compatible with option JSON.Except for some cases described below, the zones are independent of each other and are tested separately (those if 2 zones are specified - the script will send 2 requests - alternately checking one and the second zone).
For the zones you can use %RND% suffix, which allows you to generate an arbitrary string of 6 letters and numbers. (e.g.: param%RND=my_payload or param=%RND% OR A%RND%B)
You can create your own payloads, to do this, create your own folder on the '/payload/' folder, or place the payload in an existing one (e.g.: '/payload/XSS'). Allowed data format is JSON.
API testing payloads located in this directory are automatically appended with a header 'Content-Type: application/json'.
For MFD (multipart/form-data) payloads located in this directory, you must specify the BODY (required) and BOUNDARY (optional). If BOUNDARY is not set, it will be generated automatically (in this case, only the payload must be specified for the BODY, without additional data ('... Content-Disposition: form-data; ...').
If a BOUNDARY is specified, then the content of the BODY must be formatted in accordance with the RFC, but this allows for multiple payloads in BODY a separated by BOUNDARY.
Other zones are allowed in this directory (e.g.: URL, ARGS etc.). Regardless of the zone, header 'Content-Type: multipart/form-data; boundary=...' will be added to all requests.
This is a Proof Of Concept application that demostrates how AI can be used to generate accurate results for vulnerability analysis and also allows further utilization of the already super useful ChatGPT.
openai.api_key = "__API__KEY" # Enter your API keypip3 install -r requirements.txt
or
pip install -r requirements.txtSupported in both windows and linux
Profiles:
| Parameter | Return data | Description | Nmap Command |
|---|---|---|---|
p1 | json | Effective Scan | -Pn -sV -T4 -O -F |
p2 | json | Simple Scan | -Pn -T4 -A -v |
p3 | json | Low Power Scan | -Pn -sS -sU -T4 -A -v |
p4 | json | Partial Intense Scan | -Pn -p- -T4 -A -v |
p5 | json | Complete Intense Scan | -Pn -sS -sU -T4 -A -PE -PP -PS80,443 -PA3389 -PU40125 -PY -g 53 --script=vuln |
The profile is the type of scan that will be executed by the nmap subprocess. The Ip or target will be provided via argparse. At first the custom nmap scan is run which has all the curcial arguments for the scan to continue. nextly the scan data is extracted from the huge pile of data which has been driven by nmap. the "scan" object has a list of sub data under "tcp" each labled according to the ports opened. once the data is extracted the data is sent to openai API davenci model via a prompt. the prompt specifically asks for an JSON output and the data also to be used in a certain manner.
The entire structure of request that has to be sent to the openai API is designed in the completion section of the Program.
def profile(ip):
nm.scan('{}'.format(ip), arguments='-Pn -sS -sU -T4 -A -PE -PP -PS80,443 -PA3389 -PU40125 -PY -g 53 --script=vuln')
json_data = nm.analyse_nmap_xml_scan()
analize = json_data["scan"]
# Prompt about what the quary is all about
prompt = "do a vulnerability analysis of {} and return a vulnerabilty report in json".format(analize)
# A structure for the request
completion = openai.Completion.create(
engine=model_engine,
prompt=prompt,
max_tokens=1024,
n=1,
stop=None,
)
response = completion.choices[0].text
return response