{"ID":2894684,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21113","arxiv_id":"2507.21113","title":"Vulnerability Mitigation System (VMS): LLM Agent and Evaluation Framework for Autonomous Penetration Testing","abstract":"As the frequency of cyber threats increases, conventional penetration testing is failing to capture the entirety of todays complex environments. To solve this problem, we propose the Vulnerability Mitigation System (VMS), a novel agent based on a Large Language Model (LLM) capable of performing penetration testing without human intervention. The VMS has a two-part architecture for planning and a Summarizer, which enable it to generate commands and process feedback. To standardize testing, we designed two new Capture the Flag (CTF) benchmarks based on the PicoCTF and OverTheWire platforms with 200 challenges. These benchmarks allow us to evaluate how effectively the system functions. We performed a number of experiments using various LLMs while tuning the temperature and top-p parameters and found that GPT-4o performed best, sometimes even better than expected. The results indicate that LLMs can be effectively applied to many cybersecurity tasks; however, there are risks. To ensure safe operation, we used a containerized environment. Both the VMS and the benchmarks are publicly available, advancing the creation of secure, autonomous cybersecurity tools.","short_abstract":"As the frequency of cyber threats increases, conventional penetration testing is failing to capture the entirety of todays complex environments. To solve this problem, we propose the Vulnerability Mitigation System (VMS), a novel agent based on a Large Language Model (LLM) capable of performing penetration testing with...","url_abs":"https://arxiv.org/abs/2507.21113","url_pdf":"https://arxiv.org/pdf/2507.21113v1","authors":"[\"Farzana Abdulzada\"]","published":"2025-07-14T06:19:17Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}