We Urgently Need Privilege Management in MCP: A Measurement of API Usage in MCP Ecosystems
Zhihao Li, Kun Li, Boyang Ma, Minghui Xu, Yue Zhang, Xiuzhen Cheng
TL;DR
The paper addresses the security risks introduced by MCP's open plugin model by performing large-scale static analysis of 2,562 MCP plugins across 23 categories. It introduces a three-phase framework for collecting code, analyzing API usage with a multi-resource taxonmy, and reporting risk, then validates findings with real-world case studies showing privilege escalation, misinformation, and data tampering. Key contributions include a detailed resource access taxonomy, empirical measurements of API usage, and open research questions on dynamic permissions and automated trust assessment. The work highlights the need for least-privilege designs and robust privilege management to secure MCP ecosystems against real-world abuse and policy gaps.
Abstract
The Model Context Protocol (MCP) has emerged as a widely adopted mechanism for connecting large language models to external tools and resources. While MCP promises seamless extensibility and rich integrations, it also introduces a substantially expanded attack surface: any plugin can inherit broad system privileges with minimal isolation or oversight. In this work, we conduct the first large-scale empirical analysis of MCP security risks. We develop an automated static analysis framework and systematically examine 2,562 real-world MCP applications spanning 23 functional categories. Our measurements reveal that network and system resource APIs dominate usage patterns, affecting 1,438 and 1,237 servers respectively, while file and memory resources are less frequent but still significant. We find that Developer Tools and API Development plugins are the most API-intensive, and that less popular plugins often contain disproportionately high-risk operations. Through concrete case studies, we demonstrate how insufficient privilege separation enables privilege escalation, misinformation propagation, and data tampering. Based on these findings, we propose a detailed taxonomy of MCP resource access, quantify security-relevant API usage, and identify open challenges for building safer MCP ecosystems, including dynamic permission models and automated trust assessment.