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22/06/2026 10:22 am
TL;DR: 41.88% of production organisations have leaked AI or ML credentials, while 81% deploy vulnerable dependencies and 46.20% remain exposed to Log4Shell years after disclosure, according to Orca Security’s 2026 application security report, pointing to a persistent control gap across software supply chains. Security programmes still treat secrets, dependency risk, and pipeline governance as separate issues, but the attack surface is now interconnected and cumulative.
NHIMG editorial — based on content published by Orca Security: 2026 State of Application Security Report
By the numbers:
- 41.88% of production organizations have leaked AI or ML credentials.
- 81% of organizations deploy vulnerable dependencies.
- 46.20% remain exposed to Log4Shell years after disclosure.
Questions worth separating out
Q: How should security teams handle AI credentials in production environments?
A: Security teams should treat AI credentials as privileged non-human identities, not as ordinary application settings.
Q: Why do vulnerable dependencies create such a large software supply chain risk?
A: Vulnerable dependencies create large risk because build systems often trust upstream packages and then distribute that trust into many downstream environments.
Q: What do organisations get wrong about CI/CD token governance?
A: Many organisations treat CI/CD tokens as engineering convenience rather than privileged identity.
Practitioner guidance
- Classify AI credentials as privileged NHIs Put model tokens, inference keys, and billing credentials into the same inventory and access review process used for other production machine identities.
- Harden CI/CD identity before tightening application code Review build tokens, repository permissions, and merge gates as privileged access paths.
- Reduce trust in upstream packages Track which production workloads depend on high-risk libraries and monitor for malicious package activity, especially in ecosystems with fast-moving release cycles.
What's in the full report
Orca Security's full report covers the operational detail this post intentionally leaves for the source:
- Aggregated telemetry by environment type, including how the findings break down across production organizations in the United States and Europe.
- Specific examples of exposed AI and ML credentials, including the patterns seen in Hugging Face, OpenAI, Databricks, and Anthropic tokens.
- The detailed breakdown of CI/CD token permissions, commit controls, and repository settings that underpin the pipeline risk findings.
- The report's remediation-oriented commentary on how cloud-native development and AI adoption are changing the application security baseline.
👉 Read Orca Security's 2026 State of Application Security Report →
AI credentials, supply chain gaps, and what IAM teams should do?
Explore further
22/06/2026 11:15 am
AI credentials are now production NHIs, not just application secrets. Once a token can reach a model endpoint, training data, or billing surface, it behaves like a privileged machine identity with real operational impact. The governance failure is that many security programmes still classify these credentials as ordinary app config instead of high-risk access. Practitioners should manage AI tokens under the same lifecycle discipline as other privileged NHIs.
A few things that frame the scale:
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, according to The State of Non-Human Identity Security.
- 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts, according to The 2024 Non-Human Identity Security Report.
A question worth separating out:
Q: How can teams reduce the impact of exposed secrets and malicious packages?
A: Teams should combine continuous secret scanning, fast revocation, package integrity checks, and dependency inventorying across production workloads. When a secret leaks or a package is flagged, containment must happen before the credential or library can be reused in build, deployment, or runtime paths. That shortens the time attackers can exploit trust.
👉 Read our full editorial: AI credential leaks and pipeline weakness outpace app security
