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Penetration testing, a crucial industrial practice for ensuring systemsecurity, has traditionally resisted automation due to the extensive expertiserequired by human professionals. Large Language Models (LLMs) have shownsignificant advancements in various domains, and their emergent abilitiessuggest their potential to revolutionize industries. In this research, weevaluate the performance of LLMs on real-world penetration testing tasks usinga robust benchmark created from test machines with platforms. Our findingsreveal that while LLMs demonstrate proficiency in specific sub-tasks within thepenetration testing process, such as using testing tools, interpreting outputs,and proposing subsequent actions, they also encounter difficulties maintainingan integrated understanding of the overall testing scenario.  In response to these insights, we introduce PentestGPT, an LLM-empoweredautomatic penetration testing tool that leverages the abundant domain knowledgeinherent in LLMs. PentestGPT is meticulously designed with threeself-interacting modules, each addressing individual sub-tasks of penetrationtesting, to mitigate the challenges related to context loss. Our evaluationshows that PentestGPT not only outperforms LLMs with a task-completion increaseof 228.6\% compared to the \gptthree model among the benchmark targets but alsoproves effective in tackling real-world penetration testing challenges. Havingbeen open-sourced on GitHub, PentestGPT has garnered over 4,700 stars andfostered active community engagement, attesting to its value and impact in boththe academic and industrial spheres.

PentestGPT: An LLM-empowered Automatic Penetration Testing Tool