CyberRota Analysis
AI-GeneratedA vulnerability in Kibana's Machine Learning functionality allows low-privileged users to manipulate audit and notification records for arbitrary Machine Learning jobs due to insufficient authorization checks. This could lead to integrity compromises of critical data, as users can access and modify records across different spaces and for other users' jobs. Organizations utilizing Kibana for Machine Learning should prioritize addressing this issue to prevent unauthorized access and potential data integrity breaches.
Original NVD Description
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.
Related CVEs
Other vulnerabilities affecting the same vendor(s)