Watch a 10-minute conference presentation from USENIX Security '24 exploring the development and implementation of PENTESTGPT, an innovative framework for automated penetration testing using Large Language Models (LLMs). Learn how researchers from multiple institutions created a comprehensive benchmark using real-world targets to evaluate LLMs' capabilities in penetration testing tasks. Discover how PENTESTGPT's three self-interacting modules overcome context maintenance challenges and achieve a 228.6% improvement in task completion compared to GPT-3.5. Understand the framework's effectiveness in handling both benchmark targets and real-world penetration testing scenarios, as well as CTF challenges. Explore how this open-source project has gained significant traction with over 6,500 GitHub stars and fostered active community engagement, demonstrating its impact in both academic research and industrial applications.
PentestGPT: Evaluating and Harnessing Large Language Models for Automated Penetration Testing
Overview
Syllabus
USENIX Security '24 - PentestGPT: Evaluating and Harnessing Large Language Models for Automated...
Taught by
USENIX
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