MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems
For security practitioners in MCP ecosystems, this platform fills critical gaps in existing tools by enabling continuous, multi-framework threat intelligence.
MCPThreatHive automates threat intelligence for MCP-based agentic systems, addressing gaps in compositional attack modeling, continuous threat intelligence, and unified multi-framework classification. It provides a platform for data collection, AI-driven threat extraction, and risk scoring.
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.