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VulnerabilityPublished Updated

CVE-2026-18621: Red Hat OpenShift AI 2.25 · Incorrect Privilege Assignment

Affects Red Hat OpenShift AI 2.25Source CVE Database V5

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Not known to be exploited · CVSS 7.6 High.

Data as of 8 Nov 2026, 23:24 UTC

CVSS 3.1 base score
7.6High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.5% chance of exploitation in 30 days – higher than 44% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:L

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 10 Aug 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 8 Nov 2026.

What to do

Affected software

ProductAffectedFixed in
Red Hat OpenShift AI 2.25rhoai/odh-ml-pipelines-api-server-v2-rhel9Affected–Fixed in–
Red Hat OpenShift AI 2.25rhoai/odh-ml-pipelines-api-server-v2-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.3rhoai/odh-ml-pipelines-api-server-v2-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.4rhoai/odh-ml-pipelines-api-server-v2-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.4rhoai/odh-ml-pipelines-api-server-v2-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.4rhoai/odh-ml-pipelines-api-server-v2-rhel9Affected–Fixed in–

AI-suggested

To mitigate this vulnerability, enforce the Kubernetes pod security policy annotation `pod-security.kubernetes.io/enforce: restricted` on Data Science Project namespaces. Additionally, verify that the `pipeline-runner` ServiceAccount is not bound to privileged or anyuid Security Context Constraints (SCCs). These mitigations reduce the risk by preventing privileged pod creation through the vulnerable API path.

Description

A flaw was found in Data Science Pipelines (DSP). An attacker with namespace editor privileges can bypass security hardening by submitting a malicious Argo Workflow through the V1 API path. This allows the API server to create pods with elevated privileges, acting as a 'confused deputy' on behalf of the attacker. Successful exploitation grants the attacker node-root access, enabling arbitrary code execution and full control over the underlying node.

References

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