RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911448927567872 |
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| author | Nakatani, Sho |
| author_facet | Nakatani, Sho |
| contents | We present RapidPen, a fully automated penetration testing (pentesting) framework that addresses
the challenge of achieving an initial foothold (IP-to-Shell) without human intervention. Unlike prior
approaches that focus primarily on post-exploitation or require a human-in-the-loop, RapidPen
leverages large language models (LLMs) to autonomously discover and exploit vulnerabilities, starting from
a single IP address. By integrating advanced ReAct-style task planning (Re) with retrieval-augmented
knowledge bases of successful exploits, along with a command-generation and direct execution feedback loop
(Act), RapidPen systematically scans services, identifies viable attack vectors, and executes targeted
exploits in a fully automated manner.
In our evaluation against a vulnerable target from the Hack The Box platform, RapidPen achieved shell
access within 200-400 seconds at a per-run cost of approximately \$0.3-\$0.6, demonstrating a
60\% success rate when reusing prior "success-case" data. These results underscore the potential
of truly autonomous pentesting for both security novices and seasoned professionals. Organizations
without dedicated security teams can leverage RapidPen to quickly identify critical vulnerabilities,
while expert pentesters can offload repetitive tasks and focus on complex challenges.
Ultimately, our work aims to make penetration testing more accessible and cost-efficient,
thereby enhancing the overall security posture of modern software ecosystems.
Fore more information, visit this link: https://secdevlab.com/rapidpen |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16730 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents Nakatani, Sho Cryptography and Security Artificial Intelligence We present RapidPen, a fully automated penetration testing (pentesting) framework that addresses the challenge of achieving an initial foothold (IP-to-Shell) without human intervention. Unlike prior approaches that focus primarily on post-exploitation or require a human-in-the-loop, RapidPen leverages large language models (LLMs) to autonomously discover and exploit vulnerabilities, starting from a single IP address. By integrating advanced ReAct-style task planning (Re) with retrieval-augmented knowledge bases of successful exploits, along with a command-generation and direct execution feedback loop (Act), RapidPen systematically scans services, identifies viable attack vectors, and executes targeted exploits in a fully automated manner. In our evaluation against a vulnerable target from the Hack The Box platform, RapidPen achieved shell access within 200-400 seconds at a per-run cost of approximately \$0.3-\$0.6, demonstrating a 60\% success rate when reusing prior "success-case" data. These results underscore the potential of truly autonomous pentesting for both security novices and seasoned professionals. Organizations without dedicated security teams can leverage RapidPen to quickly identify critical vulnerabilities, while expert pentesters can offload repetitive tasks and focus on complex challenges. Ultimately, our work aims to make penetration testing more accessible and cost-efficient, thereby enhancing the overall security posture of modern software ecosystems. Fore more information, visit this link: https://secdevlab.com/rapidpen |
| title | RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2502.16730 |