Vulnerability from bitnami_vulndb
Published
2026-08-19 08:45
Modified
2026-08-19 09:09
Summary
Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments
Details

A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jobs and data frame analytics jobs without the trained model privilege can therefore remove a trained model from a space. The model itself is not deleted and remains available in its other spaces, and the change can be reversed by a suitably privileged user.

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{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "kibana",
        "purl": "pkg:bitnami/kibana"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "8.0.0"
            },
            {
              "fixed": "8.19.20"
            },
            {
              "introduced": "9.0.0"
            },
            {
              "fixed": "9.4.5"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-72671"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:elasticsearch:kibana:*:*:*:*:*:node.js:*:*"
    ],
    "severity": "Medium"
  },
  "details": "A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jobs and data frame analytics jobs without the trained model privilege can therefore remove a trained model from a space. The model itself is not deleted and remains available in its other spaces, and the change can be reversed by a suitably privileged user.",
  "id": "BIT-kibana-2026-72671",
  "modified": "2026-08-19T09:09:14.600Z",
  "published": "2026-08-19T08:45:15.285Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://discuss.elastic.co/t/kibana-8-19-20-and-9-4-5-security-update-esa-2026-88/389525"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-72671"
    }
  ],
  "schema_version": "1.6.2",
  "summary": "Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments"
}



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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

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.