Vulnerability-Lookup

GHSA-2RM9-5CPG-F943

Vulnerability from github – Published: 2026-08-13 21:36 – Updated: 2026-08-13 21:36

Details

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.

Severity

8.8 (High)


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

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  "affected": [],
  "aliases": [
    "CVE-2026-72642"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-823"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-08-13T20:17:24Z",
    "severity": "HIGH"
  },
  "details": "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.",
  "id": "GHSA-2rm9-5cpg-f943",
  "modified": "2026-08-13T21:36:08Z",
  "published": "2026-08-13T21:36:08Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-72642"
    },
    {
      "type": "WEB",
      "url": "https://discuss.elastic.co/t/elasticsearch-8-19-20-9-4-5-9-5-1-security-update-esa-2026-123/389504"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

CVE-2026-72642 (GCVE-0-2026-72642)

Vulnerability from cvelistv5 – Published: 2026-08-13 19:13 – Updated: 2026-08-14 03:56

Title

Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process

Summary

Severity

8.8 (High)


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

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-08-13 00:00 UTC

CWE

CWE-823 - Use of Out-of-range Pointer Offset

Assigner

elastic mitre root

References

1 reference

URL	Tags
https://discuss.elastic.co/t/elasticsearch-8-19-2…

Impacted products

1 product

Vendor	Product	Version
Elastic	Elasticsearch	Affected: 8.19.0 , ≤ 8.19.19 (semver) Affected: 9.4.0 , ≤ 9.4.4 (semver) Affected: 9.5.0 (semver)

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                "timestamp": "2026-08-13T00:00:00+00:00",
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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.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.