PortSwigger Releases 'Document My Pentest' AI Extension for Burp Suite
PortSwigger researcher Gareth Heyes introduces 'Document My Pentest,' a new open-source Burp Suite extension designed to automate the documentation phase of web security penetration testing. The tool acts as a silent co-analyst, monitoring user requests in real-time to understand probing activities and automatically generate structured records of findings. This innovation addresses the repetitive and tedious nature of manual audit trails in cybersecurity engagements. The development process highlighted specific challenges in using Large Language Models (LLMs) for vulnerability detection, such as distinguishing between harmless HTML entities and actual Cross-Site Scripting (XSS) vulnerabilities. Heyes details a strategy involving input-to-regex translation to help the AI track reflected data accurately across various encodings. Furthermore, the article emphasizes the critical role of precise prompt engineering to reduce false positives and hallucinations. By iteratively refining prompts and allowing the LLM to self-analyze its errors, the team significantly improved the tool's accuracy. This release demonstrates how AI can streamline security workflows by handling administrative tasks, allowing testers to focus on exploitation while ensuring comprehensive and consistent reporting.
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PortSwigger Releases 'Document My Pentest' AI Extension for Burp Suite
PortSwigger researcher Gareth Heyes introduces 'Document My Pentest,' a new open-source Burp Suite extension designed to automate the documentation phase of web security penetration testing. The tool acts as a silent co-analyst, monitoring user requests in real-time to understand probing activities and automatically generate structured records of findings. This innovation addresses the repetitive and tedious nature of manual audit trails in cybersecurity engagements. The development process highlighted specific challenges in using Large Language Models (LLMs) for vulnerability detection, such as distinguishing between harmless HTML entities and actual Cross-Site Scripting (XSS) vulnerabilities. Heyes details a strategy involving input-to-regex translation to help the AI track reflected data accurately across various encodings. Furthermore, the article emphasizes the critical role of precise prompt engineering to reduce false positives and hallucinations. By iteratively refining prompts and allowing the LLM to self-analyze its errors, the team significantly improved the tool's accuracy. This release demonstrates how AI can streamline security workflows by handling administrative tasks, allowing testers to focus on exploitation while ensuring comprehensive and consistent reporting.
PortSwigger Research