Vulnerability-Lookup

GHSA-CQ3G-QVXC-GHR5

Vulnerability from github – Published: 2026-03-27 00:31 – Updated: 2026-03-27 00:31

Details

A flaw was found in Red Hat OpenShift AI (RHOAI) llama-stack-operator. This vulnerability allows unauthorized access to Llama Stack services deployed in other namespaces via direct network requests, because no NetworkPolicy restricts access to the llama-stack service endpoint. As a result, a user in one namespace can access another user’s Llama Stack instance and potentially view or manipulate sensitive data.

Severity

8.1 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N

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  "affected": [],
  "aliases": [
    "CVE-2025-12805"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-653"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-03-26T22:16:25Z",
    "severity": "HIGH"
  },
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  "id": "GHSA-cq3g-qvxc-ghr5",
  "modified": "2026-03-27T00:31:20Z",
  "published": "2026-03-27T00:31:20Z",
  "references": [
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      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-12805"
    },
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    },
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    },
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}

CVE-2025-12805 (GCVE-0-2025-12805)

Vulnerability from cvelistv5 – Published: 2026-03-26 21:48 – Updated: 2026-03-31 03:55

Title

Llama-stack-k8s-operator: llama stack service exposed across namespaces due to missing networkpolicy

Summary

Severity

8.1 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-03-30 00:00 UTC

CWE

CWE-653 - Improper Isolation or Compartmentalization

Assigner

redhat redhat root

References

4 references

URL	Tags
https://access.redhat.com/errata/RHSA-2026:2106	vendor-advisoryx_refsource_REDHAT
https://access.redhat.com/errata/RHSA-2026:2695	vendor-advisoryx_refsource_REDHAT
https://access.redhat.com/security/cve/CVE-2025-12805	vdb-entryx_refsource_REDHAT
https://bugzilla.redhat.com/show_bug.cgi?id=2413101	issue-trackingx_refsource_REDHAT

Impacted products

3 products

Vendor	Product	Version
Red Hat	Red Hat OpenShift AI 2.25	Unaffected: sha256:c0d95dfbae20e87113ffb81026d379bb63ad300447df98b27d1bf9a83b084744 , < * (rpm) cpe:/a:redhat:openshift_ai:2.25::el9
Red Hat	Red Hat OpenShift AI 2.25	Unaffected: sha256:1d258fe98c2477e4256a9b936f412f2501fb7ca9e3b810347f9712e0d5ce5c92 , < * (rpm) cpe:/a:redhat:openshift_ai:2.25::el9
Red Hat	Red Hat OpenShift AI (RHOAI)	cpe:/a:redhat:openshift_ai

Date Public

2025-12-31 23:59

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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.

Detection rules are retrieved from Rulezet.

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.

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.