CVE-2026-72671: 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 Software
Event History
Frequently Asked Questions
What is the severity of CVE-2026-72671?
The severity of CVE-2026-72671 is medium with a rating of 4.3.
What is CVE-2026-72671?
CVE-2026-72671 describes a missing authorization vulnerability in Kibana that allows unauthorized modifications to machine learning trained model space assignments.
How do I fix CVE-2026-72671?
To fix CVE-2026-72671, ensure that the Kibana permissions for users are correctly configured to restrict access to machine learning capabilities.
Who is affected by CVE-2026-72671?
CVE-2026-72671 affects users of Elastic Kibana who have roles that allow creating anomaly detection jobs but do not enforce proper authorization for modifying trained models.
What are the potential consequences of CVE-2026-72671?
The potential consequences of CVE-2026-72671 include unauthorized access to and modification of machine learning models, which can impact data integrity and analytics outputs.