Same Name, Different Server: A Security Census of Silent Drift in the Model Context Protocol Ecosystem

📅 2026-09-12
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🤖 AI Summary
本文通过扫描和分析MCP注册表中的服务器,发现并量化了未授权网络暴露、静默漂移等安全问题,并提出了解决建议。
📝 Abstract
The Model Context Protocol (MCP) has become the common interface through which large language model applications reach external tools, and its public registry now distributes thousands of community-built servers with little of the vetting infrastructure that mature package ecosystems have accumulated. This paper reports a census of that ecosystem. We harvested the full public MCP registry (21,643 servers, 72,606 version records, August 2026 snapshot), fetched source code for 14,353 servers, and applied a pattern-based scanner covering an eight-class threat catalogue whose accuracy we measured against 414 hand-labeled findings. Observed prevalence is dominated by unauthenticated network exposure (9.57% of scanned servers); after correcting each class by its measured precision, 11.14% observed high-severity prevalence reduces to roughly 7.6%. The central finding concerns instability rather than any single weakness: 51.1% of multi-version servers changed what they advertise between versions, 40.6% did so silently, and 4.2% redirected their remote endpoint to a different host while keeping their registry identity, a change the protocol never surfaces to installed clients. Silent drift is associated with nearly threefold higher odds of a high-severity finding (OR = 2.96, 95% CI [2.56, 3.42]). Popularity offers only weak protection (OR = 0.78 per unit of log stars), so star counts are a poor proxy for safety. We derive concrete recommendations for registry design, client-side pinning, and scanner triage, and release an anonymized artifact.
Problem

Research questions and friction points this paper is trying to address.

Model Context Protocol
security
silent drift
unauthenticated network exposure
high-severity threats
Innovation

Methods, ideas, or system contributions that make the work stand out.

silent drift
security census
model context protocol
high-severity finding
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O
Obada Kraishan
College of Media and Communication, Texas Tech University