CVE-2026-63145

mediumpriority · track · 0OT risk · elevatedEPSS 0.003 · 18 %ileDEEURUUS
grimoire priorityP3 · Track
Exploitable?
—
no known exploitation
Likely / severe?
0.27% (18th) EPSS
CVSS 4.3 · medium
Is it mine?
28 products
Check your exposure
Who says so?
6 sources
cnnvd · enisa_euvd · fstec_bdu · osv

EPSS · scored 2026-10-23

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.

01

cvss

Technical severity, 0–10. One row per scoring source.

4.3
010

base score 0–10 · higher = more severe · hover a vector to decode

v3.14.3mediumAV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:Nsecondary · FSTEC (BDU) RU
v3.14.3mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:Nsecondary · ENISA EU
v3.14.3mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:Nsecondary · security@elastic.coexploitability 2.8 · impact 1.4
v3.14mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N/E:Usecondary · CVSS-BT (t0sche)
02

ssvc

A stakeholder decision — act, attend, or track — from exploitation and impact.

Trackcoordinatorcisa_ssvcUS
03

weaknesses

The CWE weakness classes this flaw belongs to.

04

affected products (28)

Products and version ranges known to be affected.

05

advisories (3)

Vendor and ecosystem advisories describing this.

  • WID-SEC-W-2026-2464Kibana: Mehrere SchwachstellenDeutsch2026-07-21csaf · cert_bundDE

    Ein entfernter, authentisierter Angreifer kann mehrere Schwachstellen in Kibana ausnutzen, um Sicherheitsvorkehrungen zu umgehen, einen Denial-of-Service-Zustand auszulösen oder Daten offenzulegen und zu manipulieren.

  • BIT-elk-2026-63145Incorrect Authorization in Kibana Leading to Machine Learning Audit Log Integrity Compromise2026-07-28vendor · osvUS

    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.

  • BIT-kibana-2026-63145Incorrect Authorization in Kibana Leading to Machine Learning Audit Log Integrity Compromise2026-07-28vendor · osvUS

    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.

06

also known as (7)

Other names and identifiers for this entity.

07

sources

Which sources assert this record, and when.

08

references (3)

External citations — patches, advisories, analysis.

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