Hacking, The Lazy Way: LLM Augmented Pentesting

TL;DR

This paper presents LLM Augmented Pentesting with a tool called Pentest Copilot, which integrates GPT-4-turbo and retrieval-augmented generation to automate and improve penetration testing tasks, bridging automation and human expertise.

Contribution

Introducing a novel LLM-based framework and tool for penetration testing that enhances automation, decision-making, and real-world applicability in cybersecurity.

Findings

Significantly improved task completion rates in pentesting.

Effective reduction of hallucinations through RAG.

Enhanced decision-making with chain of thought mechanisms.

Abstract

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.