Because a clean scan at publish time does not protect against later repository changes. If the platform rescans only when a skill becomes popular, attackers get a window to ship benign code, pass audit, and then mutate the repository before the next verification cycle. Continuous revalidation closes that gap.
Why delayed rescans create a marketplace blind spot
Delayed rescans turn “verified at publish time” into a weak control once listings can change after approval. In an AI agent marketplace, the trust decision is not just about the first scan, it is about whether the code, dependencies, and hosted artefacts remain the same when users install or grant access. If revalidation is tied to popularity instead of change, the attacker controls the gap.
That gap matters because marketplace listings are often treated as a signal of legitimacy. A benign first version can clear review, gain visibility, and then be altered quietly before the next inspection cycle. The longer the interval, the more time there is for malicious logic, dependency swaps, or privilege escalation paths to appear after trust has already been earned.
Delayed rescanning is especially dangerous in environments where distribution and execution are separated. The marketplace may be the trust gate, but the actual risk sits in the repository or package source that can mutate independently. For AI agent ecosystems, that can mean a change to an agent package, manifest, prompt bundle, tool configuration, or dependency graph without a fresh security decision.
How the attack window opens
Attackers exploit the delay by staging content to look safe during the initial review, then changing it after the listing is accepted. The result is a time-of-check to time-of-use problem: the platform checked one state, but users consume another. If rescans only happen when a listing becomes popular, malicious code can spread before the next scan is triggered.
This is not just a supply-chain hygiene issue, it is a trust-boundary issue. A marketplace that does not continuously revalidate is implicitly saying that the approved state is durable, even though repository content is live and mutable. The control fails when the security model assumes stasis but the delivery model allows change.
The practical consequence is that popularity can become a timing signal for attackers. They can wait until the platform’s own promotion or ranking logic makes the item more attractive, then push a harmful update while the listing still carries the reputation of a clean review.
What continuous revalidation needs to protect
Continuous revalidation should compare the thing users will actually install or invoke against the thing that was approved. That means checking the current repository state, the published artefact, and any referenced dependencies or tool definitions that can alter behaviour. A one-time approval is not enough when the package can evolve after trust is granted.
For agent marketplaces, the control should also cover the parts most likely to change behaviour without looking dramatic in a diff, such as tool permissions, execution hooks, remote fetches, and dependency references. If the platform only watches for code churn but ignores configuration drift, the scan can still miss the change that matters operationally.
The security objective is to make the approval state current at the moment of use, not merely current at the moment of submission. That is why revalidation tied to release events, content changes, or installation time is stronger than rescans driven by popularity or manual review queues.
Risk and Threat Considerations
Delayed rescans create a predictable abuse window: an item can appear trustworthy long enough to be adopted, then change before the next security check. In a marketplace context, that exposes users to repository tampering, dependency substitution, and post-approval payload changes that bypass the original review.
Failure mechanism: The platform verifies a prior version, but the marketplace serves or points to a later version with different behaviour, so the control no longer matches the delivered content.
Impact: Users can install or delegate to a malicious agent package believing it has already been vetted, which can lead to credential exposure, unauthorized actions, or further supply-chain spread.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while SLSA and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 — Vulnerable Third-Party NHI | Marketplace packages can change after approval, creating third-party trust risk. |
| NHI-01 — Improper Offboarding | Stale approvals leave previously trusted marketplace items active after risk changes. | |
| Recommendation — Revalidate third-party listings whenever repository content or dependencies change. Withdraw approval immediately when a listing no longer matches the scanned state. | ||
| OWASP Agentic AI Top 10 | ASI04 — Agentic Supply Chain Vulnerabilities | Delayed scans let mutated agent packages bypass the original trust decision. |
| Recommendation — Rescan agent artefacts on every publish, update, and dependency change. | ||
| SLSA | Supply Chain Levels for Software Artifacts | Integrity depends on verifying the artefact users consume, not only the initial submission. |
| Recommendation — Bind release approval to immutable artefact provenance and content integrity. | ||
| NIST SP 800-53 Rev 5 | SI-7 — Software, Firmware, and Information Integrity | Integrity controls must detect post-review changes before users consume the item. |
| Recommendation — Validate marketplace artefacts and rerun integrity checks after any content change. | ||
Practitioner Guidance
What to prioritise: Treat change detection as part of the approval boundary, not as a follow-up task. If the repository, artefact hash, dependency set, or tool manifest changes, the listing should move back into a verified state before it is promoted or used.
What to verify: Check that the scan target matches the runtime artefact users receive, and that rescan triggers include any content mutation, not just downloads, rating thresholds, or popularity events. If a marketplace cannot prove that linkage, its approval signal is stale.
Common mistake: Assuming a good initial scan makes later trust decisions safe. For AI agent marketplaces, the control must be lifecycle-based, because post-publication mutation is exactly where the abuse opportunity appears.
Practitioner takeaway: The right control is not “scan once, then watch interest,” it is “revalidate whenever the trusted object may have changed.”
Related resources from NHI Mgmt Group
- Why do AI-generated MCP tools and agent workflows create a different security risk than ordinary application code?
- Why do human and AI-agent access decisions create security risk when controls are not aligned to current work?
- Why does reward hacking create security risk in AI agent deployments?
- Why do AI agent requests create security risk even when the underlying developer workflow looks normal?