CVE-2026-72671: Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments

Published Aug 13, 2026
·
Updated

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

3 affected components
Elastic Kibana
Elastic Kibana<8.19.20
Elastic Kibana>=9.0.0<9.4.5

Event History

Aug 13, 2026
CVE Published
via MITRE·07:11 PM
Data Sourced
via MITRE·07:11 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·08:17 PM
DescriptionSeverityWeaknessAffected Software

Frequently Asked Questions

1

What is the severity of CVE-2026-72671?

The severity of CVE-2026-72671 is medium with a rating of 4.3.

2

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.

3

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.

4

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.

5

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.

Contact

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