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GHSA-Q5GV-XXQ5-3M7C

Vulnerability from github – Published: 2026-07-22 00:32 – Updated: 2026-07-22 00:32

Details

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.

Severity

4.3 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N

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{
  "affected": [],
  "aliases": [
    "CVE-2026-63145"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-863"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-21T23:18:02Z",
    "severity": "MODERATE"
  },
  "details": "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).\n\nA vulnerability exists in Kibana\u0027s 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\u2014including jobs in other spaces or belonging to other users\u2014by leveraging Kibana\u0027s internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.",
  "id": "GHSA-q5gv-xxq5-3m7c",
  "modified": "2026-07-22T00:32:36Z",
  "published": "2026-07-22T00:32:36Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-63145"
    },
    {
      "type": "WEB",
      "url": "https://discuss.elastic.co/t/kibana-8-19-19-9-3-8-9-4-4-security-update-esa-2026-69/388572"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N",
      "type": "CVSS_V3"
    }
  ]
}

CVE-2026-63145 (GCVE-0-2026-63145)

Vulnerability from cvelistv5 – Published: 2026-07-21 22:31 – Updated: 2026-07-22 13:21

Title

Incorrect Authorization in Kibana Leading to Machine Learning Audit Log Integrity Compromise

Summary

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.

Severity

4.3 (Medium)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator (v2.0.3)

CWE

CWE-863 - Incorrect Authorization

Assigner

elastic

References

1 reference

URL	Tags
https://discuss.elastic.co/t/kibana-8-19-19-9-3-8…

Impacted products

1 product

Vendor	Product	Version
Elastic	Kibana	Affected: 9.4.0 , ≤ 9.4.3 (semver) Affected: 9.0.0 , ≤ 9.3.7 (semver) Affected: 8.0.0 , ≤ 8.19.18 (semver)

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JSON

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                "id": "CVE-2026-63145",
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                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-07-22T13:20:48.112345Z",
                "version": "2.0.3"
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              "value": "\u003cp\u003eIncorrect 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).\n\nA vulnerability exists in Kibana\u0027s 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\u2014including jobs in other spaces or belonging to other users\u2014by leveraging Kibana\u0027s internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.\u003c/p\u003e"
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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.

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GHSA-Q5GV-XXQ5-3M7C

CVE-2026-63145 (GCVE-0-2026-63145)

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Nomenclature