Shadow MCP: Detecting and Governing Unauthorized AI Tool Servers in Enterprise Environments

Sandeep Kumar Anuguthala

doi:10.71097/IJAIDR.v17.i1.2034

Shadow MCP: Detecting and Governing Unauthorized AI Tool Servers in Enterprise Environments

Author(s)	Sandeep Kumar Anuguthala
Country	United States
Abstract	Enterprise organizations are rapidly deploying Large Language Model (LLM) agents that autonomously interact with external tools through a new infrastructure layer called the Model Context Protocol (MCP). MCP servers act as bridges between AI agents and real-world services such as databases, file systems, and APIs. While powerful, this capability has created a serious governance blind spot: organizations frequently do not know which MCP servers their AI agents are communicating with. This paper calls these unmonitored, unauthorized, or misconfigured deployments Shadow MCP Servers and treats them as a distinct and underexplored enterprise security risk. To address this, the paper designs, implements, and evaluates the Shadow MCP Detection and Governance Framework (SMDGF). The framework continuously collects signals from three complementary and independently deployable monitoring sources — network traffic analysis, endpoint process monitoring, and identity and access telemetry — and combines them into a unified risk score for each discovered MCP server. Servers that score above a configurable risk threshold are automatically enrolled in a Dynamic MCP Registry (DMR), which serves as the authoritative governance record for all MCP infrastructure and enforces access policies between AI agents and the tools they are permitted to use. The author deploys SMDGF on a real enterprise-equivalent testbed comprising 2 LLM agent instances and 8 MCP servers and evaluates it against 5 controlled Shadow MCP injection scenarios covering all four threat classes identified in the taxonomy. SMDGF achieves detection precision of 1.00, recall of 1.00, and F1-score of 1.00, with a median discovery latency of 36 seconds. The framework outperforms three baseline detection approaches while imposing less than 2% processing overhead on monitored systems, making it practical for production enterprise deployment.
Keywords	Shadow AI; Model Context Protocol Security; LLM Agent Safety; Enterprise AI Governance; Zero Trust Architecture; Network Anomaly Detection; AI Observability; Toolchain Security
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-05-09
DOI	https://doi.org/10.71097/IJAIDR.v17.i1.2034

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Shadow MCP: Detecting and Governing Unauthorized AI Tool Servers in Enterprise Environments

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