This research introduces the first comprehensive benchmark for evaluating LLMs in automated penetration testing, addressing a critical gap in cybersecurity assessment.

• Establishes standardized evaluation metrics for LLM-based penetration testing
• Demonstrates current LLM capabilities and limitations in finding security vulnerabilities
• Proposes techniques to enhance LLM performance in security contexts
• Provides a foundation for measuring progress in AI-assisted ethical hacking

This work matters because it enables organizations to better understand and leverage AI for identifying vulnerabilities before malicious actors can exploit them, potentially reducing billions in annual cybersecurity damages.

Original Paper: Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements