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VulnerabilityPublished

CVE-2026-72642: Elastic Elasticsearch · Use of Out-of-range Pointer Offset

Affects Elastic ElasticsearchSource CVE Database V5

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Not known to be exploited · CVSS 8.8 High.

Data as of 12 Nov 2026, 23:24 UTC

CVSS 3.1 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.6% chance of exploitation in 30 days – higher than 49% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 13 Aug 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 13 Nov 2026.

What to do

Affected software

ProductAffectedFixed in
ElasticElasticsearchAffected
  • >=8.19.0 <=8.19.19
  • >=9.4.0 <=9.4.4
  • =9.5.0
Fixed in–
Mavenorg.elasticsearch/elasticsearchpkg:maven/org.elasticsearch/elasticsearchOther threatsAffected
  • 8.19.0 – 9.5.0
6 affected builds
Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

Until a fix is available, restrict the ability to upload and deploy machine learning models to trusted users only. Monitor vendor communications for updates on patches or official mitigations.

Description

The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.

References

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