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.
Figures & tables
Figure 1: MCP three-tier architecture. The Host embeds an LLM and one or more MCP Clients, each maintaining a stateful session with an MCP Server over JSON-RPC 2.0.
Figure 2: MCPThreatHive four-stage pipeline architecture. Data flows left to right: intelligence sources feed into collection, items are classified by an LLM, results are stored in a relational database and knowledge graph, and exposed through interactive visualization and risk planning interfaces.