PentestGPT: Evaluating and Harnessing Large Language Models for Automated Penetration Testing
Gelei Deng, Yi Liu, Víctor Mayoral-Vilches, Peng Liu, Yuekang Li, Yuan Xu, Martin Pinzger, Stefan Rass
33rd USENIX Security Symposium · Day 1 · USENIX Security '24
The talk "PentestGPT: Evaluating and Harnessing Large Language Models for Automated Penetration Testing" introduces an innovative, open-source solution that leverages the power of Large Language Models (LLMs) to automate the complex and human-intensive process of penetration testing. Presented by Gelei Deng, this collaborative work primarily from Nanyang Technological University, with contributions from the Singapore Agency for Science Technology and Research and Al University of New South Wales, addresses a critical gap in cybersecurity: the need for reproducible and scalable penetration testing that doesn't solely rely on the subjective expertise of individual human testers.
AI review
PentestGPT presents a groundbreaking agent-based framework that effectively harnesses LLMs for end-to-end automated penetration testing, directly addressing their inherent limitations. This isn't just LLM hype; it's a meticulously engineered solution demonstrating significant performance gains on real-world targets, fundamentally shifting the landscape of security assessments and automated threats. This talk is required viewing for anyone serious about the future of offensive and defensive security.