Description-Code Inconsistency in Real-world MCP Servers: Measurement, Detection, and Security Implications
arXiv:2606.04769v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as a critical standard empowering Large Language Models (LLMs) to utilize external tools. In this ecosystem, LLMs rely on natural language descriptions provided by MCP servers to select and execute functions. This interaction implicitly assumes that tool descriptions faithfully reflect their underlying implementations, while this assumption is not mandatorily verified in practice. As a result, MCP deployments may suffer from a problem named Description-Code Inconsistency (DCI), where a tool's descrip
The proliferation of LLMs using external tools via protocols like MCP is exposing critical security vulnerabilities inherent in natural language description-based function execution, a problem growing with agentic AI adoption.
This research highlights a fundamental security flaw in how LLMs interact with external systems, posing significant risks as autonomous AI agents become more prevalent and control critical infrastructure or data.
The explicit identification of 'Description-Code Inconsistency (DCI)' as a vulnerability shifts focus towards robust verification mechanisms for AI tool descriptions, impacting development and deployment of agentic AI systems.
- · AI security researchers
- · Cybersecurity firms specializing in AI
- · Open-source security tooling developers
- · Developers neglecting security in AI tool descriptions
- · Organizations deploying agentic AI without robust validation
- · LLM ecosystems relying solely on natural language tool descriptions
Increased investment in automated DCI detection and prevention tools for AI-driven systems.
New regulatory or industry best practices emerge for validating AI tool descriptions and code consistency.
Enhanced trust models or novel secure execution environments become standard for advanced AI agents interacting with real-world infrastructure.
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Read at arXiv cs.AI