CRAIOct 27, 2025

MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers

arXiv:2510.23673v117 citationsh-index: 2
Originality Incremental advance
AI Analysis

It addresses critical security threats in MCP-based systems for developers and users, but is incremental as it builds on existing defense approaches.

The paper systematically analyzes security vulnerabilities in Model Context Protocol (MCP) servers, identifying three threat categories including agent hijacking and web vulnerabilities, and surveys defense strategies such as server-side scanning and runtime monitoring.

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.

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