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MCP-38: A Comprehensive Threat Taxonomy for Model Context Protocol Systems (v1.0)

arXiv Security Archived Mar 20, 2026 ✓ Full text saved

arXiv:2603.18063v1 Announce Type: new Abstract: The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software systems or generic LLM deployments, do not adequately cover. This paper presents MCP-38, a protocol-specific threat taxonomy consisting of 38 threat categories (MCP-01 through MCP-38). The taxonomy was derived through a systematic four-phase methodology: protocol decomposition, multi-framework cross-m

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    Computer Science > Cryptography and Security [Submitted on 18 Mar 2026] MCP-38: A Comprehensive Threat Taxonomy for Model Context Protocol Systems (v1.0) Yi Ting Shen, Kentaroh Toyoda, Alex Leung The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software systems or generic LLM deployments, do not adequately cover. This paper presents MCP-38, a protocol-specific threat taxonomy consisting of 38 threat categories (MCP-01 through MCP-38). The taxonomy was derived through a systematic four-phase methodology: protocol decomposition, multi-framework cross-mapping, real-world incident synthesis, and remediation-surface categorization. Each category is mapped to STRIDE, OWASP Top 10 for LLM Applications (2025, LLM01--LLM10), and the OWASP Top 10 for Agentic Applications (2026, ASI01--ASI10). MCP-38 addresses critical threats arising from MCP's semantic attack surface (tool description poisoning, indirect prompt injection, parasitic tool chaining, and dynamic trust violations), none of which are adequately captured by prior work. MCP-38 provides the definitional and empirical foundation for automated threat intelligence platforms. Comments: v1.0 Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.18063 [cs.CR]   (or arXiv:2603.18063v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.18063 Focus to learn more Submission history From: Kentaroh Toyoda [view email] [v1] Wed, 18 Mar 2026 02:22:41 UTC (12,555 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Mar 20, 2026
    Archived
    Mar 20, 2026
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