PentestMCP: LLM and MCP Based Multi-Agent Framework for Automated Penetration Testing

PentestMCP: LLM and MCP Based Multi-Agent Framework for Automated Penetration Testing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article PentestMCP: LLM and MCP Based Multi-Agent Framework for Automated Penetration Testing Jiqiang Zhai, Xinyi Zhou, Hong Miao, Zekun Li, Zhe Li, Hailu Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7582841/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract As information systems grow increasingly complex and cyberattack techniques continue to evolve, traditional penetration testing heavily dependent on manual expertise and operations---faces serious challenges in both efficiency and scalability. To overcome these limitations, this paper introduces PentestMCP, an end-to-end automated penetration testing framework driven by large language models (LLMs). The framework integrates three core components: a multi-agent architecture that covers the complete workflow of Information gathering, Vulnerability discovery, and exploitation; the Model Context Protocol (MCP), which standardizes tool orchestration; and retrieval-augmented generation (RAG), which strengthens contextual reasoning and reduces execution errors. In addition, PentestMCP employs a dual-path execution strategy together with a Penetration Task Graph (PTG) to achieve autonomous task decomposition, dynamic scheduling, and closed-loop control. We evaluated PentestMCP on more than one hundred real-world vulnerabilities collected from VulHub and the National Vulnerability Database, spanning diverse CWE categories and varying complexity levels. Experimental results show that PentestMCP consistently achieves higher success rates, stability, and efficiency than existing baselines, while also reducing token consumption and execution time. Using GPT-4.1, the system achieved average success rates of 87.3% for Information gathering, 62.3% for Vulnerability discovery, and 56.6% for exploitation. The findings strongly validate that an LLM and MCP-based multi-agent framework holds substantial potential for advancing the automation, scalability, and practical applicability of penetration testing. Penetration testing Large language model (LLM) Model context protocol (MCP) Retrieval-augmented generation (RAG) Multi-agent systems Cybersecurity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 26 Apr, 2026 Reviews received at journal 10 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 10 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7582841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538671885,"identity":"3e365a0d-d363-496d-b61b-b38aa6e30c75","order_by":0,"name":"Jiqiang Zhai","email":"","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiqiang","middleName":"","lastName":"Zhai","suffix":""},{"id":538671886,"identity":"a985c297-bd00-4e5d-b7a6-cf4b521b066b","order_by":1,"name":"Xinyi Zhou","email":"","orcid":"","institution":"Harbin University of Science and 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