MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols

Understand

Large Language Models (LLMs) are increasingly integrated into real-world applications via the Model Context Protocol (MCP), a universal open standard for connecting AI agents with data sources and external tools.

• While MCP enhances the capabilities of LLM-based agents, it also introduces new security risks and significantly expands their attack surface.
• In this paper, we present the first formalization of a secure MCP and its required specifications.
• Based on this foundation, we establish a comprehensive MCP security taxonomy that extends existing models by incorporating protocol-level and host-side threats, identifying 17 distinct attack types across four primary attack surfaces.