CVE-2026-72675: CWE-862 Missing Authorization in Elastic Kibana
Missing Authorization (CWE-862) in Kibana can lead to cross-space information disclosure and unauthorized data modification via Privilege Abuse (CAPEC-122). Kibana Machine Learning carries out its Elasticsearch operations with elevated internal permissions and relies on a per-request space filter to keep the machine learning data of one space separated from another. Part of the Machine Learning functionality did not apply that filter, so operations issued from one space were carried out against the machine learning data of every space in the deployment.
AI Analysis
Technical Summary
This vulnerability (CWE-862) in Kibana's Machine Learning feature occurs because certain operations bypass the intended per-request space filter that isolates machine learning data by space. As a result, operations initiated in one space can affect or disclose data from all other spaces within the same Kibana deployment. The flaw is due to missing authorization checks, leading to privilege abuse (CAPEC-122).
Potential Impact
An attacker with privileges to perform Machine Learning operations in one space can access or modify machine learning data belonging to other spaces, potentially leading to unauthorized data disclosure and integrity violations. The CVSS 3.1 score of 7.1 reflects a high impact on confidentiality with limited impact on integrity and no impact on availability.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. No official fix or temporary mitigation has been documented at this time.
CVE-2026-72675: CWE-862 Missing Authorization in Elastic Kibana
Description
Missing Authorization (CWE-862) in Kibana can lead to cross-space information disclosure and unauthorized data modification via Privilege Abuse (CAPEC-122). Kibana Machine Learning carries out its Elasticsearch operations with elevated internal permissions and relies on a per-request space filter to keep the machine learning data of one space separated from another. Part of the Machine Learning functionality did not apply that filter, so operations issued from one space were carried out against the machine learning data of every space in the deployment.
CVSS v3.1
Score 7.1high
Affected software
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Weaknesses
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
This vulnerability (CWE-862) in Kibana's Machine Learning feature occurs because certain operations bypass the intended per-request space filter that isolates machine learning data by space. As a result, operations initiated in one space can affect or disclose data from all other spaces within the same Kibana deployment. The flaw is due to missing authorization checks, leading to privilege abuse (CAPEC-122).
Potential Impact
An attacker with privileges to perform Machine Learning operations in one space can access or modify machine learning data belonging to other spaces, potentially leading to unauthorized data disclosure and integrity violations. The CVSS 3.1 score of 7.1 reflects a high impact on confidentiality with limited impact on integrity and no impact on availability.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. No official fix or temporary mitigation has been documented at this time.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- elastic
- Date Reserved
- 2026-08-10T11:17:49.704Z
- Cvss Version
- 3.1
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 6a7e1a84bf8831d539b18385
Added to database: 08/13/2026, 19:27:00 UTC
Last enriched: 08/13/2026, 19:41:16 UTC
Last updated: 08/14/2026, 02:11:29 UTC
Views: 6
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