Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments
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.
Affected Versions:
- All versions up to and including 8.19.19
- All versions from 9.0.0 up to and including 9.4.4
Affected Configurations:
- Kibana deployments running an affected version with the Machine Learning feature enabled
- Exploitation requires an authenticated Kibana user whose role grants the machine learning privileges to create anomaly detection jobs and data frame analytics jobs without granting the privilege to create trained models. The Machine Learning feature privileges available in the Kibana role management interface do not produce this combination, so exploitation depends on a custom role that assigns these privileges individually
Solutions and Mitigations:
The issue is resolved in versions 8.19.20 and 9.4.5.
For Users that Cannot Upgrade:
- There are no workarounds for this vulnerability.
Indicators of Compromise (IOC)
No specific indicators of compromise have been identified for this vulnerability.
Elastic Cloud Serverless
Due to our continuous deployment and patching model, the vulnerability described in this security advisory was remediated in our Elastic Cloud Serverless offering before the public disclosure.
Severity: CVSSv3.1: Medium ( 4.3 ) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N
CVE ID: CVE-2026-72671
Problem Type: CWE-862 - Missing Authorization