CVE-2026-63145: Incorrect Authorization in Kibana Leading to Machine Learning Audit Log Integrity Compromise
Incorrect Authorization (CWE-863) in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1).
A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management endpoint performs an insufficient authorization check. The endpoint validates only a coarse privilege level but does not verify that the requesting user has access to the specific Machine Learning job or notification resources provided in the request. As a result, a low-privileged user with Machine Learning access in any Kibana space can manipulate Machine Learning audit and notification records for arbitrary jobs—including jobs in other spaces or belonging to other users—by leveraging Kibana's internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.
Affected Software
Event History
Frequently Asked Questions
What is the severity of CVE-2026-63145?
The severity of CVE-2026-63145 is medium with a score of 4.3.
How do I fix CVE-2026-63145?
To fix CVE-2026-63145, upgrade to the latest version of Elastic Kibana that addresses this vulnerability.
What types of access are affected by CVE-2026-63145?
CVE-2026-63145 affects functionalities that are not properly constrained by Access Control Lists (ACLs) in Kibana's Machine Learning.
What can be compromised due to CVE-2026-63145?
CVE-2026-63145 can lead to an integrity compromise of Machine Learning audit and notification records in Kibana.
What is the main issue identified in CVE-2026-63145?
The main issue in CVE-2026-63145 is incorrect authorization leading to unauthorized access to machine learning functionalities.