BoxPwnr
BoxPwnr is an experimental project that leverages Large Language Models (LLMs) to autonomously solve HackTheBox (HTB) machines. This innovative tool aims to explore the capabilities of LLMs in the realm of cybersecurity and penetration testing.
Key Features:
- Autonomous Problem Solving: Utilizes LLMs to automate the process of solving HTB machines, reducing the need for manual intervention.
- Docker Environment: Runs all commands in a Docker container with Kali Linux, ensuring a secure and isolated environment for testing.
- Iterative Learning: Collects data from each attempt to improve the model's performance over time.
- Comprehensive Command Options: Offers a variety of command line options for customization, including model selection, execution control, and debugging.
- Testing Infrastructure: Includes a robust testing framework using pytest, allowing for easy testing and validation of the tool's functionality.
Benefits:
- Efficiency: Significantly speeds up the process of solving complex security challenges by automating command execution.
- Research and Development: Provides a platform for researchers to study the effectiveness of LLMs in cybersecurity tasks.
- User-Friendly: Designed with clear instructions and options, making it accessible for both beginners and experienced users.
Highlights:
- Focuses on the intersection of AI and cybersecurity, showcasing the potential of LLMs in real-world applications.
- Encourages ethical use and compliance with HackTheBox's terms of service, promoting responsible research practices.
