MCP threat-modeling and attack-surface auditing for enterprise AI systems
Automated threat-modeling engine and continuous security auditing for Model Context Protocol (MCP) implementations in enterprise LLM deployments, focusing on inverted client-server architectures and context-injection attack vectors.
The problem
Enterprise teams deploying MCP-connected LLM systems lack specialized tooling to identify and model the novel attack paths created by inverted client-server protocols and context-protocol vulnerabilities; current threat-modeling tools (STRIDE, PASTA) and API-security scanners miss MCP-specific risks, leaving organizations exposed to regulatory and operational risk.
Who has it: Fortune 500 and mid-market enterprises (500+ employees) in regulated verticals (financial services, healthcare, defense, energy) deploying LLM agents with MCP integrations and requiring security audit trails.
Why now: NSA published MCP security guidance (May 2026) establishing baseline threat taxonomy; major LLM vendors are shipping MCP connectors; early enterprise deployments are entering production without security validation; regulatory bodies will soon mandate MCP threat-modeling as part of AI governance frameworks.
Where this came from
2 public sources behind this idea.
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