Is CVE-2026-83495 exploited in the wild?

No. Junglewise Threat Intelligence has no report of CVE-2026-83495 being exploited in the wild.

How severe is CVE-2026-83495?

Informational severity, published 7 November 2026.

Which products and versions are affected?

Red Hat OpenShift AI Trainer v2 (Technology Preview).

How is CVE-2026-83495 attacked?

It is attacked with local access, in a hosted service.

Am I affected by CVE-2026-83495?

Check whether you run Red Hat OpenShift AI at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

CVE-2026-83495: Red Hat Trainer v2 TrainJob API secret injection privilege escalation

CVE-2026-83495 · Severity: info · Published 2026-11-07

A flaw in Red Hat OpenShift AI's Trainer v2 component allows users with permissions to create training jobs to read sensitive secrets from the cluster namespace, potentially escalating their privileges. An attacker with job-creation permissions can inject secret references into environment variables, exfiltrating credentials and other confidential data if the job creation policy is broader than the secret read policy. This affects Technology Preview features in Red Hat OpenShift AI.

Technologies: Red Hat OpenShift AI. Vendors: Red Hat.

Quick answers

Is CVE-2026-83495 exploited in the wild?: No. Junglewise Threat Intelligence has no report of CVE-2026-83495 being exploited in the wild.
How severe is CVE-2026-83495?: Informational severity, published 7 November 2026.
Which products and versions are affected?: Red Hat OpenShift AI Trainer v2 (Technology Preview).
Is there a fix for CVE-2026-83495?: No fixed version is recorded yet. Align Role-Based Access Control (RBAC) for users creating TrainJobs with their Secret read permissions. Alternatively, restrict the valueFrom.secretKeyRef field within the validating webhook to an allowlist of permitted secret names.</anigation> <parameter name="timeline">[{"kind":"disclosed","at":"2026-11-07"}] If you run it, Junglewise drafts a mitigation that fits your setup and applies it once you approve, with senior security engineers behind it.
How is CVE-2026-83495 attacked?: It is attacked with local access, in a hosted service.
Am I affected by CVE-2026-83495?: Check whether you run Red Hat OpenShift AI at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

How to fix

Align Role-Based Access Control (RBAC) for users creating TrainJobs with their Secret read permissions. Alternatively, restrict the valueFrom.secretKeyRef field within the validating webhook to an allowlist of permitted secret names.</anigation> <parameter name="timeline">[{"kind":"disclosed","at":"2026-11-07"}]

Technical details

The vulnerability is an improper privilege management issue (CWE-269) in the Trainer v2 TrainJob API where a user can inject valueFrom.secretKeyRef into environment variable definitions to bypass RBAC restrictions and read namespace secrets. The attack requires the target user to have broader TrainJob creation permissions than their intended Secret read access within the same namespace. Exploitation is a local/direct vector requiring existing cluster access and no fix is currently available.

Affected products

Red Hat OpenShift AI Trainer v2 (Technology Preview)

References

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