BreachSeek - Multi-Agent Automated Penetration Tester
1. Executive Summary:
This document briefs on BreachSeek, a novel AI-driven multi-agent software platform designed to automate penetration testing for websites and networks. It leverages Large Language Models (LLMs) integrated through LangChain and LangGraph in Python to identify vulnerabilities, simulate cyberattacks, execute exploits, and generate security reports. Preliminary evaluations demonstrate its effectiveness in exploiting vulnerabilities in local networks. BreachSeek addresses the limitations of traditional, manual penetration testing by providing a scalable and efficient solution that can adapt to evolving cybersecurity threats.
2. Problem Statement:
Traditional penetration testing methods are:
Time-consuming and labor-intensive: Requiring significant human expertise and time.
Ineffective against rapidly evolving threats: Unable to keep pace with the growing sophistication and diversity of cyberattacks.
Limited in scope: Struggling to manage the complexity of modern digital environments.
The document highlights a "critical need for an automated solution that can efficiently identify and exploit vulnerabilities across diverse systems without extensive human intervention."
3. Proposed Solution: BreachSeek
BreachSeek aims to solve the limitations of traditional penetration testing by:
Automating the penetration testing workflow: Using AI agents to identify vulnerabilities, simulate attacks, and execute exploits with minimal human intervention.
Enhancing accuracy and comprehensiveness: Leveraging LLMs to improve the quality of results and provide a robust solution to cybersecurity threats.
Providing a scalable solution: Deployable in a wide range of environments, from small to large-scale networks.
Key Features:
AI-Driven, Multi-Agent Architecture: Utilizes multiple AI agents, each with a distinct focus, to manage complexity and breadth of tasks involved in penetration testing. This addresses context window limitations of LLMs. "One of the key technical innovations in Breach-Seek is the use of multiple AI agents, each with a distinct focus, to manage the complexity and breadth of tasks involved in penetration testing."
Graph-Based Approach (LangGraph): Enables the creation of specialized nodes that communicate, distributing tasks for enhanced performance, customization, and mitigation of context window limitations.
Specific Agents:
Supervisor: Generates action plans and identifies subsequent steps.
Pentester: Accesses tools (shell, Python) in a Kali Linux environment to execute commands and report output.
Recorder: Maintains a summary of actions and generates a final report.
Evaluator: Assesses the output quality and task completion accuracy.
Scalability: Can be deployed in multiple containers to efficiently manage large volumes of data and complex network architectures.
Comprehensive Reporting: Generates a formatted PDF report documenting the entire penetration testing process.
Web UI: Developed using NextJS and FastAPI for user interaction.
4.Technical Details:
Implementation Environment: Docker-based Kali Linux environment hosted on RunPod.
LLMs Used:Development Phase: Anthropic’s Claude 3.5 Sonnet model.
Testing and Future Deployment: Plans to use Llama 3.1, an open-source model for customized fine-tuning.
Testing Methodology: Exploiting vulnerabilities on a Metasploitable 2 machine hosted on the same local network.
5.Results and Evaluation:
Qualitative Assessment: Initial evaluations were qualitative.
Successful Exploitation: The model successfully exploited a Metasploitable 2 machine, achieving root access with approximately 150,000 tokens. "In our preliminary testing, the model successfully exploited a Metasploitable 2 machine, achieving root access with approximately 150,000 tokens."
Versatility: Minor adjustments to the workflow and system prompts allow the system to address challenges in diverse domains.
Future Quantitative Measures: Plans to incorporate quantitative measures using established benchmarks like OWASP Web Security Testing Guide (WSTG) and Offensive Security Certified Professional (OSCP) exam content.
BreachSeek: AI-Driven Automated Penetration Testing Platform
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Briefing Document: BreachSeek - Multi-Agent Automated Penetration Tester
Document Version: 1.0 Date: October 26, 2024 Source: "2409.03789v1.pdf" (Excerpts)
1. Executive Summary:
This document briefs on BreachSeek, a novel AI-driven multi-agent software platform designed to automate penetration testing for websites and networks. It leverages Large Language Models (LLMs) integrated through LangChain and LangGraph in Python to identify vulnerabilities, simulate cyberattacks, execute exploits, and generate security reports. Preliminary evaluations demonstrate its effectiveness in exploiting vulnerabilities in local networks. BreachSeek addresses the limitations of traditional, manual penetration testing by providing a scalable and efficient solution that can adapt to evolving cybersecurity threats.
2. Problem Statement:
Traditional penetration testing methods are:
Time-consuming and labor-intensive: Requiring significant human expertise and time.
Ineffective against rapidly evolving threats: Unable to keep pace with the growing sophistication and diversity of cyberattacks.
Limited in scope: Struggling to manage the complexity of modern digital environments.
The document highlights a "critical need for an automated solution that can efficiently identify and exploit vulnerabilities across diverse systems without extensive human intervention."
3. Proposed Solution: BreachSeek
BreachSeek aims to solve the limitations of traditional penetration testing by:
Automating the penetration testing workflow: Using AI agents to identify vulnerabilities, simulate attacks, and execute exploits with minimal human intervention.
Enhancing accuracy and comprehensiveness: Leveraging LLMs to improve the quality of results and provide a robust solution to cybersecurity threats.
Providing a scalable solution: Deployable in a wide range of environments, from small to large-scale networks.
Key Features:
AI-Driven, Multi-Agent Architecture: Utilizes multiple AI agents, each with a distinct focus, to manage complexity and breadth of tasks involved in penetration testing. This addresses context window limitations of LLMs. "One of the key technical innovations in Breach-Seek is the use of multiple AI agents, each with a distinct focus, to manage the complexity and breadth of tasks involved in penetration testing."
Graph-Based Approach (LangGraph): Enables the creation of specialized nodes that communicate, distributing tasks for enhanced performance, customization, and mitigation of context window limitations.
Specific Agents:Supervisor: Generates action plans and identifies subsequent steps.
Pentester: Accesses tools (shell, Python) in a Kali Linux environment to execute commands and report output.
Recorder: Maintains a summary of actions and generates a final report.
Evaluator: Assesses the output quality and task completion accuracy.
Scalability: Can be deployed in multiple containers to efficiently manage large volumes of data and complex network architectures.
Comprehensive Reporting: Generates a formatted PDF report documenting the entire penetration testing process.
Web UI: Developed using NextJS and FastAPI for user interaction.
4. Technical Details:
Implementation Environment: Docker-based Kali Linux environment hosted on RunPod.
LLMs Used:Development Phase: Anthropic’s Claude 3.5 Sonnet model.
Testing and Future Deployment: Plans to use Llama 3.1, an open-source model for customized fine-tuning.
Testing Methodology: Exploiting vulnerabilities on a Metasploitable 2 machine hosted on the same local network.
5. Results and Evaluation:
Qualitative Assessment: Initial evaluations were qualitative.
Successful Exploitation: The model successfully exploited a Metasploitable 2 machine, achieving root access with approximately 150,000 tokens. "In our preliminary testing, the model successfully exploited a Metasploitable 2 machine, achieving root access with approximately 150,000 tokens."
Versatility: Minor adjustments to the workflow and system prompts allow the system to address challenges in diverse domains.
Future Quantitative Measures: Plans to incorporate quantitative measures using established benchmarks like OWASP Web Security Testing Guide (WSTG) and Offensive Security Certified Professional (OSCP) exam content.
6. Future Work:
Human Intervention: Integrate a user permission system that prompts for approval before executing specific tools or commands. "To enhance the safety and control of Breach-Seek during penetration testing, future work will focus on integrating a user permission system that prompts for approval before executing specific tools or commands."
Fine-Tuning: Fine-tune the model using specialized cybersecurity data (web scraping cybersecurity write-ups and penetration testing reports).
Retrieval-Augmented Generation (RAG): Incorporate a RAG system to reference a vector database of penetration testing techniques and strategies. "This approach will allow Breach-Seek to reference a vector database containing useful penetration testing techniques, strategies, and past experiences."
Dynamic and Engaging Responses: Introduce dynamic response modes to cater to various user preferences (fun, relaxed, task-focused).
Multi-Modality: Introduce multi-modal input support, allowing users to submit images and videos for analysis.
7. Literature Review Summary:
The literature review highlights the growing use of LLMs in cybersecurity and penetration testing. Tools like PentestGPT are improving task completion rates compared to earlier models like GPT-3.5 and GPT-4. Generative AI offers opportunities for rapid vulnerability identification and novel attack vector simulation, but challenges remain in grasping broader context. BreachSeek addresses these challenges through its multi-agent architecture and direct command execution within a terminal. LLMs are also being used in defensive measures, such as risk management and automated vulnerability fixing.
Key Takeaways from Literature Review:
PentestGPT: An LLM-empowered tool showing significant improvement in automated penetration testing.
Generative AI Benefits: Rapid vulnerability identification and creative attack vector simulation.
Generative AI Challenges: Incomplete understanding of testing context.
8. Conclusion:
BreachSeek represents a significant advancement in automated cybersecurity penetration testing by combining AI-driven agents with the scalability required in modern network environments. It is positioned as a powerful and adaptable tool in the evolving landscape of AI-driven cybersecurity solutions. Future enhancements will focus on improving safety, accuracy, and user experience.
