Can a language model actually help with real-world security testing? 🛡️🤖
PentestGPT is a new tool designed to serve as a virtual assistant during penetration testing. Developed by a researcher known as GreyDGL, it builds on OpenAI’s GPT-4 to help guide professionals through standard pentest workflows. Unlike traditional automation tools that execute payloads or scan for vulnerabilities directly, PentestGPT focuses on reasoning and decision-making support based on user input.
The tool is structured around common engagement types like web application testing and external infrastructure enumeration. It doesn’t directly interface with targets—it relies on the tester to collect and provide data, such as HTTP responses or error messages. From there, it helps interpret results, suggest next steps, and generate reports aligned with methodologies like OWASP or PTES.
For example, if presented with a suspicious response header or authentication behavior, PentestGPT can analyze it and suggest tailored test cases like token replay or path traversal checks. It also maintains context throughout the interaction, preserving key findings and adapting its advice accordingly.
Still, it's not a replacement for human testers. Its strength lies in augmenting expertise, reducing time spent on documentation loops or repetitive analysis. On the downside, its dependency on accurate input and lack of active scanning limits its autonomy.
As of now, it’s available as an open-source project on GitHub. It's part of a broader trend of applying large language models not just to content generation, but also toward structured, technical workflows—including cybersecurity.
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