MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914116583555072 |
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| author | Wang, Bin Liu, Zexin Yu, Hao Yang, Ao Huang, Yenan Guo, Jing Cheng, Huangsheng Li, Hui Wu, Huiyu |
| author_facet | Wang, Bin Liu, Zexin Yu, Hao Yang, Ao Huang, Yenan Guo, Jing Cheng, Huangsheng Li, Hui Wu, Huiyu |
| contents | The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and external data sources and tools. While MCP significantly reduces development complexity and enhances agent capabilities, its openness and extensibility introduce critical security vulnerabilities that threaten system trustworthiness and user data protection. This paper systematically analyzes the security landscape of MCP-based systems, identifying three principal threat categories: (1) agent hijacking attacks stemming from protocol design deficiencies; (2) traditional web vulnerabilities in MCP servers; and (3) supply chain security. To address these challenges, we comprehensively survey existing defense strategies, examining both proactive server-side scanning approaches, ranging from layered detection pipelines and agentic auditing frameworks to zero-trust registry systems, and runtime interaction monitoring solutions that provide continuous oversight and policy enforcement. Our analysis reveals that MCP security fundamentally represents a paradigm shift where the attack surface extends from traditional code execution to semantic interpretation of natural language metadata, necessitating novel defense mechanisms tailored to this unique threat model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers Wang, Bin Liu, Zexin Yu, Hao Yang, Ao Huang, Yenan Guo, Jing Cheng, Huangsheng Li, Hui Wu, Huiyu Cryptography and Security Artificial Intelligence The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and external data sources and tools. While MCP significantly reduces development complexity and enhances agent capabilities, its openness and extensibility introduce critical security vulnerabilities that threaten system trustworthiness and user data protection. This paper systematically analyzes the security landscape of MCP-based systems, identifying three principal threat categories: (1) agent hijacking attacks stemming from protocol design deficiencies; (2) traditional web vulnerabilities in MCP servers; and (3) supply chain security. To address these challenges, we comprehensively survey existing defense strategies, examining both proactive server-side scanning approaches, ranging from layered detection pipelines and agentic auditing frameworks to zero-trust registry systems, and runtime interaction monitoring solutions that provide continuous oversight and policy enforcement. Our analysis reveals that MCP security fundamentally represents a paradigm shift where the attack surface extends from traditional code execution to semantic interpretation of natural language metadata, necessitating novel defense mechanisms tailored to this unique threat model. |
| title | MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23673 |