EUVD-2026-58258
GHSA-92c8-vpgp-w44c
Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N
Requires authenticated low-privilege session (PR:L, AV:N); only integrity is affected as the reversible space-removal causes no data loss or confidentiality exposure.
Primary rating from Vendor (elastic).
CVSS VectorVendor: elastic
Lifecycle Timeline
2DescriptionCVE.org
A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jobs and data frame analytics jobs without the trained model privilege can therefore remove a trained model from a space. The model itself is not deleted and remains available in its other spaces, and the change can be reversed by a suitably privileged user.
AnalysisAI
Kibana's Machine Learning space management feature performs an incomplete authorization check when removing a saved object from a space, accepting trained models as targets while only verifying privileges for anomaly detection jobs and data frame analytics jobs. Authenticated users whose roles grant ML job creation rights but lack the trained model privilege can therefore remove a trained model from the active space, despite having no legitimate authority over trained models. No public exploit is identified and this vulnerability is not listed in CISA KEV; real-world impact is bounded by the reversibility of the action and the non-deletion of the underlying model.
Technical ContextAI
Kibana implements a fine-grained RBAC system across Elastic Stack features, including distinct privilege classes for Machine Learning object types: anomaly detection jobs, data frame analytics jobs, and trained models. The vulnerability resides in the remove-saved-object-from-space operation within the ML capability, where the authorization handler verifies only the first two privilege classes and omits a check for trained model privileges. This is a textbook CWE-862 (Missing Authorization) flaw - authorization logic is present for some ML object types but structurally absent for trained models. The affected product is identified by CPE cpe:2.3:a:elastic:kibana:*:*:*:*:*:*:*:*, covering all Kibana versions prior to the patched releases named in vendor advisory ESA-2026-88.
RemediationAI
Upgrade Kibana to version 8.19.20 (8.x branch) or 9.4.5 (9.x branch) as directed by vendor advisory ESA-2026-88 at https://discuss.elastic.co/t/kibana-8-19-20-and-9-4-5-security-update-esa-2026-88/389525. As a compensating control prior to patching, administrators should audit all custom Kibana roles to identify users who hold create anomaly detection job or data frame analytics job privileges without the corresponding trained model privilege, and consider temporarily revoking or restricting those combined grant configurations to reduce the exploitable population. Note that role audits must span all spaces individually. Enabling Kibana audit logging and alerting on remove-from-space events for ML trained model objects provides detection coverage while patching is scheduled. Removing ML job creation rights from users who do not actively use them reduces attack surface permanently, with the trade-off of requiring role re-grants when legitimate ML work resumes.
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Same weakness CWE-862 – Missing Authorization
View allSame technique Authentication Bypass
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External POC / Exploit Code
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