MCP Under the Lens: A Multi-Framework Security Analysis of the Model Context Protocol

The Model Context Protocol (MCP) has emerged as the de facto standard for connecting AI agents to external tools and data sources, yet its security posture remains empirically uncharted despite rapid production adoption. We present the first systematic security analysis of MCP through OWASP-aligned evaluation of 71 authentication and security issues from the official GitHub repository, spanning March 2025 through January 2026. Using AI-powered natural language processing with structured validation (Claude Opus 4.6 + Pydantic schemas), we extracted security concepts, mapped issues to OWASP Top 10 frameworks (LLM, Agentic, MCP), and analyzed contributor engagement patterns. Our findings reveal authentication architecture as the dominant threat, with MCP07 (Insufficient Authentication & Authorization) accounting for 72% of primary mappings. While the community demonstrates active governance—including participation from OAuth specification authors and a 66% issue closure rate—critical vulnerabilities remain unresolved, including a CRITICAL-severity phishing attack exploiting weak server identity verification. Cross-framework analysis exposes risk propagation across protocol (MCP), agent (ASI), and model (LLM) layers, with 14 ASI and 5 LLM secondary mappings validating defense-in-depth requirements. We contribute evidence-based recommendations prioritized by OWASP category impact, including mandating RFC 9207 issuer validation, implementing sender-bound tokens, and establishing cryptographic client verification. This work provides a reproducible methodology for security discourse analysis in AI infrastructure protocols and informs both MCP specification development and broader agent security practices.