An empirical taxonomy of 11 top-level categories and 27 subcategories of runtime faults in MCP servers, derived via open coding of GitHub threads and validated by a survey of 55 developers.
A measurement study of model context protocol ecosystem
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 7roles
background 1polarities
background 1representative citing papers
Empirical study of 2,214 MCP servers finds 9.93% of 19,200 description-code pairs inconsistent via a new static-analysis-plus-LLM-prompting framework, with security implications.
Presents a component-centric PoC dataset of malicious MCP servers and a two-stage behavioral deviation detector Connor achieving 94.6% F1-score.
Catalogues five MCP server architectural patterns observed across fifteen servers, plus anti-patterns and quantitative measurements on tool handling and overhead.
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
VIPER-MCP detects and exploits taint-style vulnerabilities in Model Context Protocol servers via anchor-query static analysis and feedback-driven prompt evolution, uncovering 106 zero-day vulnerabilities across 39,884 repositories with 67 CVEs assigned.
Introduces Task2MCP dataset and T2MRec model for recommending MCP servers to LLM agents based on task semantics and engineering constraints.
citing papers explorer
-
Description-Code Inconsistency in Real-world MCP Servers: Measurement, Detection, and Security Implications
Empirical study of 2,214 MCP servers finds 9.93% of 19,200 description-code pairs inconsistent via a new static-analysis-plus-LLM-prompting framework, with security implications.
-
From Component Manipulation to System Compromise: Understanding and Detecting Malicious MCP Servers
Presents a component-centric PoC dataset of malicious MCP servers and a two-stage behavioral deviation detector Connor achieving 94.6% F1-score.
-
Behavioral Integrity Verification for AI Agent Skills
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
-
VIPER-MCP: Detecting and Exploiting Taint-Style Vulnerabilities in Model Context Protocol Servers
VIPER-MCP detects and exploits taint-style vulnerabilities in Model Context Protocol servers via anchor-query static analysis and feedback-driven prompt evolution, uncovering 106 zero-day vulnerabilities across 39,884 repositories with 67 CVEs assigned.