GHSA-2RM9-5CPG-F943
Vulnerability from github – Published: 2026-08-13 21:36 – Updated: 2026-08-13 21:36
VLAI
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)
{
"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"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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.
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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.
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.
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