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About this paper
Yasod Ginige, Akila Niroshan, Sajal Jain, Suranga Seneviratne
AutoPentester is an LLM agent-based framework that significantly enhances automated penetration testing by dynamically generating attack strategies and minimizing human intervention. It achieved a 27.0% higher average subtask completion rate and an 85.7% reduction in repetitive loops compared to PentestGPT, alongside a 39.5% improvement in vulnerability coverage on custom virtual machines.
Key insights
- Integrating specialized LLM agents for distinct functions like summarization, strategic planning, and command generation (with RAG) enables a higher degree of automation and accuracy in pentesting.
- A "findings-oriented Chain-of-Thought (CoT) reasoning" dynamically updating a Pentest Tree allows the system to develop more logical and adaptive attack strategies, mimicking human expert thought processes.
- Modules for repetition identification and results verification are crucial for preventing redundant actions, ensuring command correctness, and enabling self-correction, thereby significantly reducing the need for human intervention.
Summary by alphaXiv.
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