Shadow AI skills and plugins: what security teams are missing

TL;DR: Shadow AI is increasingly hiding in plain markdown files and installable plugins rather than obvious chatbots, with skills and plugins spreading outside central approval while exposing secrets, code execution, and audit gaps, according to Willow. The governance problem is not discovery alone but the collapse of review, ownership, and least-privilege assumptions at the point where agents inherit capability.

Editorial analysis by NHI Mgmt Group, based on content published by Willow: “Shadow AI: The Skills and Plugins Nobody Approved”.

Key questions

Q: What breaks when AI skills and plugins are added outside central approval?

A: Governance breaks at the point of capability inheritance.

Q: Why do unapproved AI skills and plugins create more risk than a visible chatbot?

A: A chatbot is usually the visible interface, but a skill or plugin changes what the agent can actually do.

Q: How can security teams detect shadow AI without relying on gateway logs alone?

A: They need inventory, local telemetry and policy coverage across all the places agents run, including IDEs, terminals and browser-based assistants.

Practitioner guidance

• Build a complete capability inventory Discover every skill, plugin, and connected MCP surface across endpoints and browsers, then record who added it, what it can touch, and where it runs.
• Classify capability by action scope Set approval rules around the data, tools, and operations each skill or plugin can reach, not just whether the underlying agent is allowed.
• Remove secrets from local skill files Scan markdown-based skills and adjacent config for API keys, tokens, and other secrets, then revoke anything exposed outside a secrets manager.

Bottom line: Unapproved AI skills and plugins create a shadow AI surface that bypasses the approval models most organisations still use for apps and chatbots.

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