CVE-2026-72671 (GCVE-0-2026-72671)

Vulnerability from cvelistv5 – Published: 2026-08-13 19:11 – Updated: 2026-08-13 20:33
VLAI
Title
Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments
Summary
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.
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 · CISA-ADP (v2.0.3)
Decision recorded 2026-08-13 20:25 UTC
CWE
Assigner
References
Impacted products
Vendor Product Version
Elastic Kibana Affected: 8.0.0 , ≤ 8.19.19 (semver)
Affected: 9.0.0 , ≤ 9.4.4 (semver)
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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

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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.
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  • 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.



Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.