CVE-2026-5483 (GCVE-0-2026-5483)
Vulnerability from cvelistv5 – Published: 2026-04-10 17:33 – Updated: 2026-07-15 00:43
VLAI
EPSS
VEX
Title
Odh-dashboard: odh dashboard kubernetes service account exposure
Summary
A flaw was found in odh-dashboard in Red Hat Openshift AI. This vulnerability in the `odh-dashboard` component of Red Hat OpenShift AI (RHOAI) allows for the disclosure of Kubernetes Service Account tokens through a NodeJS endpoint. This could enable an attacker to gain unauthorized access to Kubernetes resources.
Severity
8.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-10 18:33 UTC
CWE
- CWE-201 - Insertion of Sensitive Information Into Sent Data
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://access.redhat.com/errata/RHSA-2026:7397 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:7398 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:7403 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:7404 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/security/cve/CVE-2026-5483 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2454764 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
5 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Red Hat | Red Hat OpenShift AI 2.16 |
Unaffected:
1775230902 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.16::el8 |
||
| Red Hat | Red Hat OpenShift AI 2.25 |
Unaffected:
1775234711 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.25::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.2 |
Unaffected:
1775523049 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.2::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1775239958 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
Date Public
2026-04-10 17:16
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}
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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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