MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910131521847296 |
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| author | Shen, Yi Ting Toyoda, Kentaroh Leung, Alex |
| author_facet | Shen, Yi Ting Toyoda, Kentaroh Leung, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13849 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems Shen, Yi Ting Toyoda, Kentaroh Leung, Alex Cryptography and Security Artificial Intelligence 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. |
| title | MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2604.13849 |