Supply Chain Threats in the MCP Ecosystem: Attack Vectors and Mitigation Strategies

초록

The rise of autonomous AI agents powered by large language models (LLMs) has been accompanied by new frameworks for integrating these agents with external tools and data. One such framework is Anthropic’s Model Context Protocol (MCP), a recently introduced open standard that enables AI assistants to connect with a wide variety of external systems. While MCP unlocks powerful capabilities for agentic AI, it also dramatically expands the supply chain threat surface. In this paper, we investigate supply chain threats in the MCP ecosystem, a rapidly emerging security frontier as community-driven development and open-source MCP servers become prevalent. We identify and categorize major supply chain threats in the MCP ecosystem and validate representative scenarios via proof-of-concept attacks. In particular, we demonstrate how malicious MCP servers, as well as hostile data inputs, can be used to intentionally trigger unauthorized or harmful behaviors, such as sensitive data exfiltration or security policy violations. Finally, we outline two complementary avenues for mitigating MCP supply-chain threats. First, a specification- and document-based validation framework addresses code-driven threats by statically verifying that an MCP tool’s implementation aligns with its declared interface and behavior. This method has been prototyped and shown to detect functional inconsistencies in real-world tools. Second, we propose the conceptual design of lightweight runtime validation agents—supervisory components that monitor prompt flows, tool responses, and runtime context to intercept data-driven threats. Together, these layers—proven static analyzers and envisioned runtime validators—form a cohesive foundation for securing AI-agent infrastructures in the MCP ecosystem. By identifying novel attack surfaces and proposing layered defenses, our work represents an early step toward framing supply chain threat dimensions in MCP and AI agent security, and contributes to the ongoing discourse on mitigation strategies for AI-integrated supply chains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

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