How should teams handle agent-generated skills before they are trusted?

What makes an agent-generated skill unsafe before trust is established?

An agent-generated skill should be treated as code that can act, not as harmless configuration text. The security issue is that the skill may smuggle in hidden behaviour, unsafe dependencies, or an execution path that the original prompt never made explicit. That means trust has to be earned through the same kinds of checks you would use for other unvetted software artefacts.

When teams skip that gate, they tend to discover problems only after the skill has already been attached to an agent, shared broadly, or executed in production. The review point matters because the skill layer can become a shortcut around normal change control, especially when the skill is presented as a productivity helper rather than a deployable component.

For agentic systems, that distinction is important enough that OWASP Agentic Skills Top 10 (AST10) treats the skill layer as a security boundary, not just a convenience feature.

Which checks should happen before a skill is trusted?

Three checks are essential. Code review looks for hidden logic, unsafe defaults, prompt-conditioned branches, and anything that expands the skill beyond the declared purpose. Dependency scanning looks for risky transitive packages, malicious packages, and version drift that can undermine the skill after approval. Network egress checks confirm where the skill can reach, so an apparently local capability cannot quietly call out to an unapproved service.

Those controls work together because each one catches a different failure mode. Code review can miss a dependency chain. Dependency scanning can miss runtime behaviour. Egress review can miss what a dependency does after install. Teams that only validate one layer often end up approving a skill that looks narrow on paper but is broad in practice.

That is why review should focus on the actual artefact that will run, not on the user-facing description of the skill. If the skill will execute commands, call APIs, or fetch content, the trust decision should be based on the full execution path and not on intent alone.

AI Coding Agents Security Guide covers the same practical pattern in a coding context, where hidden packages, over-scoped access, and sandbox bypasses can turn a useful helper into an unsafe artefact.

How should governance control promotion into production?

Promotion should be explicit, approved, and reversible. A skill should remain untrusted until someone accountable has confirmed its behaviour, its dependencies, and its outbound connections, and until the team is satisfied that the skill cannot obtain more authority than it needs. The important governance question is not whether the skill is useful, but whether it is safe to activate as a capability with production reach.

That approval step should be tied to ownership. Someone has to own the skill definition, someone has to approve changes, and someone has to be able to withdraw the skill quickly if later inspection shows a bad dependency, an unexpected network path, or behaviour that conflicts with the intended use case.

At scale, the main failure is not one malicious skill, but many small approvals that never received enough scrutiny. Teams should prefer a narrow allowlist model, where a skill becomes active only after passing a known review path, rather than a broad publish-and-monitor approach that assumes problems will be caught later.

AI Agent Authorisation Guide is useful here because it frames approval as a per-action and least-privilege decision, which is the right model for deciding when a skill may become active.

Risk and Threat Considerations

Agent-generated skills can be prompt-influenced supply-chain objects. If an attacker can shape the generation process, the resulting skill may embed a backdoor, unsafe dependency, or network path that persists after the prompt is forgotten. The danger is not just accidental insecurity, but deliberate abuse of the trust teams place in generated artefacts.

Failure mechanism: A skill is approved on the basis of intent or convenience, then executes with hidden dependencies, excessive reach, or outbound calls that bypass normal review and create a latent compromise path.

Impact: The organisation can inherit unauthorised execution, data exfiltration, credential exposure, or downstream privilege abuse once the skill becomes an active capability.

Agentic AI Security Guide is relevant because it treats tool and identity abuse as real attack surfaces, which is exactly the mindset needed when evaluating generated skills.

Practitioner Guidance

What to prioritise: Treat the first production activation as the highest-risk moment. If the skill has not been reviewed as code, dependency-scanned, and egress-checked, do not let it inherit broad agent permissions just because it was generated by a trusted workflow.

What to verify: Confirm the skill’s effective execution path, not only its declared purpose. The most common miss is a skill that behaves correctly in the happy path but contains an unexpected package, fetches remote content, or opens an outbound channel during a less obvious branch.

Practitioner takeaway: The safest default is to approve agent-generated skills only when they are already understandable as reviewable software, because trust should follow inspection, not creation method.

Related resources from NHI Mgmt Group

• How should security teams handle AI agent visibility?
• What are Agent Skills and how do they enhance AI performance?
• When do AI agent credentials create more risk than they reduce?
• How should teams think about AI agent privileges?