Treat it as untrusted whenever the skill can change after installation, depends on external packages, or can reach sensitive systems through delegated access. Passing review is only a point-in-time decision. If the runtime state can drift, the trust decision must remain open and continuously verified.
When a Reviewed Skill Is Still a Moving Trust Boundary
An ai agent skill should still be treated as untrusted when its behavior can change after approval, because review only validates one snapshot of code, dependencies, and policy. The practical question is not whether the skill looked safe at review time, but whether the runtime version, package graph, or delegated access path can later become different from what was approved.
A skill that can auto-update, fetch remote content, call external services, or load third-party packages has a moving trust boundary. Even if the initial package passed review, later dependency changes, compromised registries, or altered configuration can change what the skill actually executes. That is why runtime verification matters as much as pre-deployment review.
Delegated access raises the same issue. If a skill can act through a user session, a service account, an OAuth grant, or another inherited permission set, then the review decision has to account for the maximum reachable impact, not just the intended task. The relevant AI Agent Authorisation Guide is useful here because it frames access as task-scoped and per-action, which is the right mental model for skills that can reach sensitive systems.
What Makes the Runtime State Unsafe
The core failure mode is drift. A skill may be reviewed as benign, then become unsafe because an upstream package is replaced, a plugin is added, a prompt template changes, or the skill starts forwarding data to a new endpoint. In agentic systems, that drift can happen without a fresh human review, which means the original approval no longer describes the thing that is actually running.
Teams should also treat a skill as untrusted when it can influence other tools or chained actions beyond its original scope. The risk is not limited to direct code execution. A skill that can trigger searches, write files, submit requests, or invoke another capability can create indirect impact even when its own code seems harmless. The Agentic AI Security Guide is relevant because it treats tools, orchestration, and identity as one attack surface rather than separate concerns.
Skills that depend on external packages deserve the same caution because dependency trust is transitive. A clean review of the top-level skill does not prove the imported modules, hosted assets, or runtime calls remain trustworthy later. For that reason, package integrity, source pinning, and runtime attestation matter whenever the skill can update or expand its dependency chain after installation.
How to Decide Whether Trust Should Stay Open
If the skill can change without going back through the same approval gate, the trust decision should stay open. That includes skills with dynamic code paths, remote configuration, marketplace updates, or operator-controlled prompts that materially alter behavior. The safest stance is to treat approval as conditional, then require continuous verification of the current state before allowing sensitive actions.
If the skill can reach production data, administrative consoles, or other sensitive systems, the default should be least privilege with explicit action boundaries. The most useful control lens is whether the skill can do something irreversible, broad, or difficult to attribute. When it can, the fact that it passed review once is not enough to make it trusted indefinitely. The Zero Trust for AI Agents guide maps well to this decision because it emphasizes continuous verification, no standing privilege, and per-action policy enforcement.
That same logic applies when the skill sits inside a larger agent or platform. If the parent system can silently route new context, swap models, or change tool permissions, the skill inherits a more uncertain trust posture than the review record suggests. In those cases, teams should rely on current observability, not historical approval, to decide whether the skill remains safe to use.
Risk and Threat Considerations
Review drift creates a practical security exposure because the approved artifact and the live artifact may diverge. An attacker does not need to beat the original review if they can alter dependencies, configuration, or delegated access after approval. That makes skills with mutable behavior, external dependency chains, or broad inherited permissions a persistent trust problem rather than a one-time assessment.
Failure mechanism: The skill changes after approval through updates, dependency substitution, configuration drift, or expanded delegated access, so the reviewed trust decision no longer matches runtime reality.
Impact: The skill may access sensitive systems, move data, or trigger downstream actions under a stale approval, which can turn a previously acceptable skill into an unreviewed and potentially high-impact one.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Skills that inherit or expand delegated access create agent privilege risk. |
| ASI04 — Agentic Supply Chain Vulnerabilities | Mutable skills and dependencies can change after review through the supply chain. | |
| ASI02 — Tool Misuse | A skill can remain dangerous when it can invoke tools beyond its intended scope. | |
| Recommendation — Enforce per-action authorization and bound the skill’s delegated privileges. Pin and verify skill dependencies and update sources before allowing execution. Restrict tool access to the minimum set needed for the approved task. | ||
| NIST SP 800-53 Rev 5 | SA-11 — Developer Testing and Evaluation | Reviewed skills still need validation against changed code and dependencies. |
| AC-6 — Least Privilege | Delegated access should be narrowed when a skill can reach sensitive systems. | |
| Recommendation — Re-test the skill after changes that can alter behavior or reach. Limit the skill to the smallest permissions needed for its current task. | ||
Practitioner Guidance
What to verify: Confirm whether the skill is immutable, whether dependencies are pinned, and whether any runtime path can change the code or the permissions without a new approval step. If any of those are true, classify the skill as conditionally trusted at best, not fully trusted.
Decision rule: If the skill can update itself, call external packages, or inherit delegated access to sensitive systems, require continuous verification and bounded authorization rather than relying on the original review outcome. If it cannot drift and its privilege is tightly constrained, the review can carry more weight.
Practitioner takeaway: Treat review as evidence about the skill at one point in time, not as a lasting trust guarantee, and let runtime changeability determine whether the trust decision must remain open.