CVE-2026-18951 (GCVE-0-2026-18951)
Vulnerability from cvelistv5 – Published: 2026-08-10 20:45 – Updated: 2026-08-27 15:06
VLAI
EPSS
VEX
Title
Odh-training-operator-rhel9: [trainer v2 security] trn-02: rhoai overlay aggregates trainjobs crud into standard edit clusterrole
Summary
A flaw was found in the Red Hat OpenShift AI (RHOAI) overlay for the training operator. The RHOAI overlay incorrectly aggregates `trainjobs` management permissions into the native Kubernetes `edit ClusterRole`. This allows any user with `edit ClusterRole` permissions in a namespace to create, modify, and delete `TrainJobs`. When combined with a separate vulnerability (TRN-01) that permits arbitrary pod configurations, a remote attacker with namespace editor privileges could exploit this to escalate privileges, potentially leading to arbitrary code execution.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-12 15:36 UTC
CWE
- CWE-284 - Improper Access Control
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://access.redhat.com/errata/RHSA-2026:53262 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:53263 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:60520 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/security/cve/CVE-2026-18951 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2511187 | issue-trackingx_refsource_REDHAT |
Impacted products
3 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1785188461 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1784814352 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787361677 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
Date Public
2026-08-10 18:54
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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