MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems

The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing frameworks are inadequately equipped to address. We present MCPThreatHive, an open-source platform that automates the end-to-end lifecycle of MCP threat intelligence: from continuous, multi-source data collection through AI-driven threat extraction and classification, to structured knowledge graph storage and interactive visualization. The platform operationalizes the MCP-38 threat taxonomy, a curated set of 38 MCP-specific threat patterns mapped to STRIDE, OWASP Top 10 for LLM Applications, and OWASP Top 10 for Agentic Applications. A composite risk scoring model provides quantitative prioritization. Through a comparative analysis of representative existing MCP security tools, we identify three critical coverage gaps that MCPThreatHive addresses: incomplete compositional attack modeling, absence of continuous threat intelligence, and lack of unified multi-framework classification.

MCP-38: A Comprehensive Threat Taxonomy for Model Context Protocol Systems (v1.0)

The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software systems or generic LLM deployments, do not adequately cover. This paper presents MCP-38, a protocol-specific threat taxonomy consisting of 38 threat categories (MCP-01 through MCP-38). The taxonomy was derived through a systematic four-phase methodology: protocol decomposition, multi-framework cross-mapping, real-world incident synthesis, and remediation-surface categorization. Each category is mapped to STRIDE, OWASP Top 10 for LLM Applications (2025, LLM01--LLM10), and the OWASP Top 10 for Agentic Applications (2026, ASI01--ASI10). MCP-38 addresses critical threats arising from MCP's semantic attack surface (tool description poisoning, indirect prompt injection, parasitic tool chaining, and dynamic trust violations), none of which are adequately captured by prior work. MCP-38 provides the definitional and empirical foundation for automated threat intelligence platforms.