Auth bypass in Elastic Kibana

CVE-2026-63145

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.

Vulnerability class: Broken Access Control

EPSS: 0.003 (18.7th percentile) — read the EPSS interpretation.

CVSS v3 metric

CVSS v3 base score 4.3 (Medium). Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N.

Affected products

Weakness classification (CWE)

References

Frequently asked questions

What is CVE-2026-63145?
CVE-2026-63145 is a medium-severity vulnerability in Elastic Kibana, classified under Incorrect Authorization. CVSS score: 4.3/10. Published 2026-07-21.
How severe is CVE-2026-63145?
Medium severity. CVSS v3 base score is 4.3 out of 10.