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14/09/2026 10:10 am
TL;DR: Credential weaknesses now play a role in 89% of security investigations, and traditional secret scanning can miss live exposures while flooding teams with false positives, according to Apiiro. The real control problem is not discovery alone but context, ownership, and rapid revocation before exposed secrets are weaponised.
NHIMG editorial — based on content published by Apiiro: LLMjacking: How Attackers Hijack AI Using Compromised NHIs
By the numbers:
- Traditional secret-detection tools can produce up to 80% false positive alerts.
Questions worth separating out
Q: How should security teams handle exposed secrets without breaking production?
A: Security teams should first map the secret to its owning non-human identity, then confirm whether it is live, what it can access, and whether dependent systems can tolerate revocation.
Q: Why do exposed AI secrets create more risk than ordinary cloud credentials?
A: AI secrets often sit between data sources, model access, and downstream business systems, so a single key can expose prompts, outputs, and linked datasets while also enabling access to adjacent services.
Q: When does secret scanning fail to reduce real risk?
A: It fails when it produces alerts without context or response.
Practitioner guidance
- Map every secret to an accountable owner Assign one operational owner for each credential so leaked secrets can be rotated and revoked without delay.
- Prioritise only live and reachable credentials Validate whether a found secret is active, where it is deployed, and what systems it can unlock before escalating severity.
- Move detection left into developer workflows Use pre-commit hooks, IDE checks, and merge-time enforcement so secrets are blocked before they enter Git history.
What's in the full article
Apiiro's full article covers the operational detail this post intentionally leaves for the source:
- Workflow-by-workflow examples of how secrets leak through Git history, CI/CD logs, container layers, and collaboration platforms
- A practical breakdown of context-aware prioritisation and how risk graphs can route findings to the right owner
- Implementation detail on rotation cadence, ephemeral access, and how to block secrets before merge
- Apiiro's framing of ASPM as the intelligence layer for secret detection and remediation
👉 Read Apiiro's analysis of why secret leakage still drives modern AI pipeline risk →
Secret leakage in AI pipelines: what security teams need to fix?
Explore further
14/09/2026 10:11 am
Credential leakage is now an identity governance failure, not just an AppSec defect. Once a secret can authenticate to a cloud service, pipeline, or AI workload, it is functioning as a non-human identity with real authority. That means lifecycle control, ownership, and revocation matter as much as detection. The governance model must treat exposed credentials as active identity assets, not static strings.
A question worth separating out:
Q: Should organisations prioritise secret rotation or secret discovery first?
A: They should do both, but rotation is the control that reduces immediate exposure when a secret is already live. Discovery tells you where the problem is. Rotation and revocation tell you whether the credential still works. If you can only choose one urgent action after a leak, invalidate the credential and confirm it is no longer active.
👉 Read our full editorial: Secret leakage is still the fastest path into modern AI pipelines
