MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
TLDR
MCPThreatHive is an open-source platform that automates threat intelligence for Model Context Protocol (MCP) agentic systems, addressing critical security gaps.
Key contributions
- Automates end-to-end MCP threat intelligence, from data collection to visualization.
- Leverages AI for threat extraction/classification and knowledge graph storage.
- Operationalizes MCP-38 taxonomy, mapped to STRIDE and OWASP Top 10 frameworks.
- Provides composite risk scoring and fills gaps in existing MCP security tools.
Why it matters
MCP-based agentic systems face new, unaddressed security threats. MCPThreatHive fills critical gaps by automating continuous threat intelligence. It offers a unified framework to classify and prioritize emerging threats, significantly enhancing security.
Original Abstract
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.
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