用大模型辅助渗透测试，提升效率与准确性
Hacking, The Lazy Way: LLM Augmented Pentesting
- 用大模型自动完成工具使用、结果解读等子任务
- 任务完成率显著提升，且减少幻觉错误
- 适合安全工程师快速开展渗透测试
本研究提出一种名为「LLM增强型渗透测试」的新范式，通过名为「Pentest Copilot」的工具实现。该工具基于GPT-4-turbo模型，将大语言模型融入渗透测试流程，解决传统自动化在该领域难以落地的问题。其核心能力包括自动调用测试工具、解析输出结果并建议下一步操作。通过引入「思维链」机制优化令牌使用，提升决策准确性；结合检索增强生成（RAG），有效降低幻觉，确保知识时效性。系统支持浏览器内直接运行，构建了完整的在线渗透测试平台。实验表明，该方法显著提升了任务完成率，有效应对真实世界挑战，为网络安全领域带来实质性进展。
原文摘要 · Abstract (English)
In our research, we introduce a new concept called "LLM Augmented Pentesting" demonstrated with a tool named "Pentest Copilot," that revolutionizes the field of ethical hacking by integrating Large Language Models (LLMs) into penetration testing workflows, leveraging the advanced GPT-4-turbo model. Our approach focuses on overcoming the traditional resistance to automation in penetration testing by employing LLMs to automate specific sub-tasks while ensuring a comprehensive understanding of the overall testing process. Pentest Copilot showcases remarkable proficiency in tasks such as utilizing testing tools, interpreting outputs, and suggesting follow-up actions, efficiently bridging the gap between automated systems and human expertise. By integrating a "chain of thought" mechanism, Pentest Copilot optimizes token usage and enhances decision-making processes, leading to more accurate and context-aware outputs. Additionally, our implementation of Retrieval-Augmented Generation (RAG) minimizes hallucinations and ensures the tool remains aligned with the latest cybersecurity techniques and knowledge. We also highlight a unique infrastructure system that supports in-browser penetration testing, providing a robust platform for cybersecurity professionals. Our findings demonstrate that LLM Augmented Pentesting can not only significantly enhance task completion rates in penetration testing but also effectively addresses real-world challenges, marking a substantial advancement in the cybersecurity domain.
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