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Thief Raccoon is a tool designed for educational purposes to demonstrate how phishing attacks can be conducted on various operating systems. This tool is intended to raise awareness about cybersecurity threats and help users understand the importance of security measures like 2FA and password management.
```bash git clone https://github.com/davenisc/thief_raccoon.git cd thief_raccoon
```bash apt install python3.11-venv
```bash python -m venv raccoon_venv source raccoon_venv/bin/activate
```bash pip install -r requirements.txt
Usage
```bash python app.py
After running the script, you will be presented with a menu to select the operating system. Enter the number corresponding to the OS you want to simulate.
If you are on the same local network (LAN), open your web browser and navigate to http://127.0.0.1:5000.
If you want to make the phishing page accessible over the internet, use ngrok.
Using ngrok
Download ngrok from ngrok.com and follow the installation instructions for your operating system.
Expose your local server to the internet:
Get the public URL:
After running the above command, ngrok will provide you with a public URL. Share this URL with your test subjects to access the phishing page over the internet.
How to install Ngrok on Linux?
```bash curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc \ | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null \ && echo "deb https://ngrok-agent.s3.amazonaws.com buster main" \ | sudo tee /etc/apt/sources.list.d/ngrok.list \ && sudo apt update \ && sudo apt install ngrok
```bash ngrok config add-authtoken xxxxxxxxx--your-token-xxxxxxxxxxxxxx
Deploy your app online
Put your app online at ephemeral domain Forwarding to your upstream service. For example, if it is listening on port http://localhost:8080, run:
```bash ngrok http http://localhost:5000
Example
```bash python app.py
```bash Select the operating system for phishing: 1. Windows 10 2. Windows 11 3. Windows XP 4. Windows Server 5. Ubuntu 6. Ubuntu Server 7. macOS Enter the number of your choice: 2
Open your browser and go to http://127.0.0.1:5000 or the ngrok public URL.
Disclaimer
This tool is intended for educational purposes only. The author is not responsible for any misuse of this tool. Always obtain explicit permission from the owner of the system before conducting any phishing tests.
License
This project is licensed under the MIT License. See the LICENSE file for details.
ScreenShots
Credits
Developer: @davenisc Web: https://davenisc.com
With the rapidly increasing variety of attack techniques and a simultaneous rise in the number of detection rules offered by EDRs (Endpoint Detection and Response) and custom-created ones, the need for constant functional testing of detection rules has become evident. However, manually re-running these attacks and cross-referencing them with detection rules is a labor-intensive task which is worth automating.
To address this challenge, I developed "PurpleKeep," an open-source initiative designed to facilitate the automated testing of detection rules. Leveraging the capabilities of the Atomic Red Team project which allows to simulate attacks following MITRE TTPs (Tactics, Techniques, and Procedures). PurpleKeep enhances the simulation of these TTPs to serve as a starting point for the evaluation of the effectiveness of detection rules.
Automating the process of simulating one or multiple TTPs in a test environment comes with certain challenges, one of which is the contamination of the platform after multiple simulations. However, PurpleKeep aims to overcome this hurdle by streamlining the simulation process and facilitating the creation and instrumentation of the targeted platform.
Primarily developed as a proof of concept, PurpleKeep serves as an End-to-End Detection Rule Validation platform tailored for an Azure-based environment. It has been tested in combination with the automatic deployment of Microsoft Defender for Endpoint as the preferred EDR solution. PurpleKeep also provides support for security and audit policy configurations, allowing users to mimic the desired endpoint environment.
To facilitate analysis and monitoring, PurpleKeep integrates with Azure Monitor and Log Analytics services to store the simulation logs and allow further correlation with any events and/or alerts stored in the same platform.
TLDR: PurpleKeep provides an Attack Simulation platform to serve as a starting point for your End-to-End Detection Rule Validation in an Azure-based environment.
The project is based on Azure Pipelines and requires the following to be able to run:
You can provide a security and/or audit policy file that will be loaded to mimic your Group Policy configurations. Use the Secure File option of the Library in Azure DevOps to make it accessible to your pipelines.
Refer to the variables file for your configurable items.
Deploying the infrastructure uses the Azure Pipeline to perform the following steps:
Currently only the Atomics from the public repository are supported. The pipelines takes a Technique ID as input or a comma seperate list of techniques, for example:
The logs of the simulation are ingested into the AtomicLogs_CL table of the Log Analytics Workspace.
There are currently two ways to run the simulation:
This pipeline will deploy a fresh platform after the simulation of each TTP. The Log Analytic workspace will maintain the logs of each run.
Warning: this will onboard a large number of hosts into your EDR
A fresh infrastructure will be deployed only at the beginning of the pipeline. All TTP's will be simulated on this instance. This is the fastests way to simulate and prevents onboarding a large number of devices, however running a lot of simulations in a same environment has the risk of contaminating the environment and making the simulations less stable and predictable.
T3SF is a framework that offers a modular structure for the orchestration of events based on a master scenario events list (MSEL) together with a set of rules defined for each exercise (optional) and a configuration that allows defining the parameters of the corresponding platform. The main module performs the communication with the specific module (Discord, Slack, Telegram, etc.) that allows the events to present the events in the input channels as injects for each platform. In addition, the framework supports different use cases: "single organization, multiple areas", "multiple organization, single area" and "multiple organization, multiple areas".
To use the framework with your desired platform, whether it's Slack or Discord, you will need to install the required modules for that platform. But don't worry, installing these modules is easy and straightforward.
To do this, you can follow this simple step-by-step guide, or if you're already comfortable installing packages with pip, you can skip to the last step!
FreshRSS 
# Python 3.6+ required
python -m venv .venv # We will create a python virtual environment
source .venv/bin/activate # Let's get inside it
pip install -U pip # Upgrade pipOnce you have created a Python virtual environment and activated it, you can install the T3SF framework for your desired platform by running the following command:
pip install "T3SF[Discord]" # Install the framework to work with Discordor
pip install "T3SF[Slack]" # Install the framework to work with SlackThis will install the T3SF framework along with the required dependencies for your chosen platform. Once the installation is complete, you can start using the framework with your platform of choice.
We strongly recommend following the platform-specific guidance within our Read The Docs! Here are the links:
We created this framework to simplify all your work!
$ docker run --rm -t --env-file .env -v $(pwd)/MSEL.json:/app/MSEL.json base4sec/t3sf:slackInside your .env file you have to provide the SLACK_BOT_TOKEN and SLACK_APP_TOKEN tokens. Read more about it here.
There is another environment variable to set, MSEL_PATH. This variable tells the framework in which path the MSEL is located. By default, the container path is /app/MSEL.json. If you change the mount location of the volume then also change the variable.
$ docker run --rm -t --env-file .env -v $(pwd)/MSEL.json:/app/MSEL.json base4sec/t3sf:discordInside your .env file you have to provide the DISCORD_TOKEN token. Read more about it here.
There is another environment variable to set, MSEL_PATH. This variable tells the framework in which path the MSEL is located. By default, the container path is /app/MSEL.json. If you change the mount location of the volume then also change the variable.
Once you have everything ready, use our template for the main.py, or modify the following code:
Here is an example if you want to run the framework with the Discord bot and a GUI.
from T3SF import T3SF
import asyncio
async def main():
await T3SF.start(MSEL="MSEL_TTX.json", platform="Discord", gui=True)
if __name__ == '__main__':
asyncio.run(main())Or if you prefer to run the framework without GUI and with Slack instead, you can modify the arguments, and that's it!
Yes, that simple!
await T3SF.start(MSEL="MSEL_TTX.json", platform="Slack", gui=False)If you need more help, you can always check our documentation here!
DoSinator is a versatile Denial of Service (DoS) testing tool developed in Python. It empowers security professionals and researchers to simulate various types of DoS attacks, allowing them to assess the resilience of networks, systems, and applications against potential cyber threats.
Clone the repository:
git clone https://github.com/HalilDeniz/DoSinator.gitNavigate to the project directory:
cd DoSinatorInstall the required dependencies:
pip install -r requirements.txtusage: dos_tool.py [-h] -t TARGET -p PORT [-np NUM_PACKETS] [-ps PACKET_SIZE]
[-ar ATTACK_RATE] [-d DURATION] [-am {syn,udp,icmp,http,dns}]
[-sp SPOOF_IP] [--data DATA]
optional arguments:
-h, --help Show this help message and exit.
-t TARGET, --target TARGET
Target IP address.
-p PORT, --port PORT Target port number.
-np NUM_PACKETS, --num_packets NUM_PACKETS
Number of packets to send (default: 500).
-ps PACKET_SIZE, --packet_size PACKET_SIZE
Packet size in bytes (default: 64).
-ar ATTACK_RATE, --attack_rate ATTACK_RATE
Attack rate in packets per second (default: 10).
-d DURATION, --duration DURATION
Duration of the attack in seconds.
-am {syn,udp,icmp,htt p,dns}, --attack-mode {syn,udp,icmp,http,dns}
Attack mode (default: syn).
-sp SPOOF_IP, --spoof-ip SPOOF_IP
Spoof IP address.
--data DATA Custom data string to send.target_ip: IP address of the target system.target_port: Port number of the target service.num_packets: Number of packets to send (default: 500).packet_size: Size of each packet in bytes (default: 64).attack_rate: Attack rate in packets/second (default: 10).duration: Duration of the attack in seconds.attack_mode: Attack mode: syn, udp, icmp, http (default: syn).spoof_ip: Spoof IP address (default: None).data: Custom data string to send.The usage of the Dosinator tool for attacking targets without prior mutual consent is illegal. It is the end user's responsibility to obey all applicable local, state, and federal laws. The author assumes no liability and is not responsible for any misuse or damage caused by this program.
By using Dosinator, you agree to use this tool for educational and ethical purposes only. The author is not responsible for any actions or consequences resulting from misuse of this tool.
Please ensure that you have the necessary permissions to conduct any form of testing on a target network. Use this tool at your own risk.
Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.
If you have any questions, comments, or suggestions about Dosinator, please feel free to contact me:
