CVE-2025-12103 (GCVE-0-2025-12103)
Vulnerability from cvelistv5 – Published: 2025-10-28 13:31 – Updated: 2026-08-05 14:27
VLAI
EPSS
VEX
Title
Openshift-ai: trusty ai grants all authenticated users to list pods in any namespace
Summary
A flaw was found in Red Hat Openshift AI Service. The TrustyAI component is granting all service accounts and users on a cluster permissions to get, list, watch any pod in any namespace on the cluster.
TrustyAI is creating a role `trustyai-service-operator-lmeval-user-role` and a CRB `trustyai-service-operator-default-lmeval-user-rolebinding` which is being applied to `system:authenticated` making it so that every single user or service account can get a list of pods running in any namespace on the cluster
Additionally users can access all `persistentvolumeclaims` and `lmevaljobs`
Severity
5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-28 13:43 UTC
CWE
- CWE-266 - Incorrect Privilege Assignment
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://access.redhat.com/errata/RHSA-2025:21117 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:10184 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/security/cve/CVE-2025-12103 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2405966 | issue-trackingx_refsource_REDHAT |
Impacted products
3 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Red Hat | Red Hat OpenShift AI 2.25 |
Unaffected:
1776243252 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.25::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.0 |
Unaffected:
v3.0.0-1762251316 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.0::el9 |
||
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
Date Public
2025-10-28 09:00
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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