Elk: Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments (CVE-2026-72671)
Kibana contains a vulnerability in its Machine Learning capability where insufficient authorization checks allow users with privileges to create anomaly detection and data frame analytics jobs—but without trained model privileges—to remove trained machine learning models from a space. The trained model itself is not deleted and remains accessible in other spaces. This modification can be reversed by users with appropriate privileges. The issue affects Kibana versions from 8.0.0 up to but not including 8.19.20, and from 9.0.0 up to but not including 9.4.5. A patch is available to address this vulnerability.
AI Analysis
Technical Summary
The vulnerability in Kibana's Machine Learning feature arises because the authorization check when removing a saved object from a space only verifies privileges related to anomaly detection jobs and data frame analytics jobs, but not the trained model privilege. Consequently, a user granted permissions to create anomaly detection and data frame analytics jobs, but lacking trained model privileges, can remove a trained machine learning model from a space. The trained model itself is not deleted and remains available in other spaces. This change can be undone by users with sufficient privileges. The vulnerability affects Kibana versions >=8.0.0 <8.19.20 and >=9.0.0 <9.4.5. A patch is available.
Potential Impact
An unauthorized user with specific job creation privileges but without trained model privileges can remove trained machine learning models from a Kibana space. Although the model is not deleted and remains accessible elsewhere, this unauthorized modification could disrupt space-specific configurations or workflows. The vulnerability does not lead to deletion or loss of the model and can be reversed by authorized users.
Mitigation Recommendations
A patch is available for this vulnerability. Users should upgrade Kibana to versions 8.19.20 or later, or 9.4.5 or later, where this issue is fixed. There are no vendor advisories indicating that no action is required or that the issue is already mitigated. Apply the official fix to prevent unauthorized removal of trained models from spaces.
Elk: Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments (CVE-2026-72671)
Description
Kibana contains a vulnerability in its Machine Learning capability where insufficient authorization checks allow users with privileges to create anomaly detection and data frame analytics jobs—but without trained model privileges—to remove trained machine learning models from a space. The trained model itself is not deleted and remains accessible in other spaces. This modification can be reversed by users with appropriate privileges. The issue affects Kibana versions from 8.0.0 up to but not including 8.19.20, and from 9.0.0 up to but not including 9.4.5. A patch is available to address this vulnerability.
Affected software
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AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The vulnerability in Kibana's Machine Learning feature arises because the authorization check when removing a saved object from a space only verifies privileges related to anomaly detection jobs and data frame analytics jobs, but not the trained model privilege. Consequently, a user granted permissions to create anomaly detection and data frame analytics jobs, but lacking trained model privileges, can remove a trained machine learning model from a space. The trained model itself is not deleted and remains available in other spaces. This change can be undone by users with sufficient privileges. The vulnerability affects Kibana versions >=8.0.0 <8.19.20 and >=9.0.0 <9.4.5. A patch is available.
Potential Impact
An unauthorized user with specific job creation privileges but without trained model privileges can remove trained machine learning models from a Kibana space. Although the model is not deleted and remains accessible elsewhere, this unauthorized modification could disrupt space-specific configurations or workflows. The vulnerability does not lead to deletion or loss of the model and can be reversed by authorized users.
Mitigation Recommendations
A patch is available for this vulnerability. Users should upgrade Kibana to versions 8.19.20 or later, or 9.4.5 or later, where this issue is fixed. There are no vendor advisories indicating that no action is required or that the issue is already mitigated. Apply the official fix to prevent unauthorized removal of trained models from spaces.
Technical Details
- Gcve Source
- db.gcve.eu
- Osv Id
- BIT-kibana-2026-72671
- Osv Schema Version
- 1.6.2
- Aliases
- ["CVE-2026-72671"]
- Ecosystems
- ["Bitnami"]
- Database Specific Severity
- Medium
- Cvss Version
- null
Threat ID: 6a85b4aaacd9273b49250ee2
Added to database: 08/19/2026, 13:50:34 UTC
Last enriched: 08/19/2026, 14:02:09 UTC
Last updated: 08/19/2026, 18:52:00 UTC
Views: 2
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