{
  "schemaVersion": 1,
  "attribution": [
    {
      "source": "CISA Known Exploited Vulnerabilities",
      "url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog",
      "licence": "US government work"
    },
    {
      "source": "GitHub Advisory Database",
      "url": "https://github.com/advisories",
      "licence": "CC BY 4.0"
    },
    {
      "source": "Have I Been Pwned",
      "url": "https://haveibeenpwned.com/",
      "licence": "CC BY 4.0"
    },
    {
      "source": "CVE Program (CVE records)",
      "url": "https://www.cve.org/Legal/TermsOfUse",
      "licence": "CVE Terms of Use",
      "notice": "Copyright © 1999-2026, The MITRE Corporation. CVE is a trademark and the CVE logo is a registered trademark of The MITRE Corporation.",
      "terms": "CVE Usage: MITRE hereby grants you a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare derivative works of, publicly display, publicly perform, sublicense, and distribute Common Vulnerabilities and Exposures (CVE™). Any copy you make for such purposes is authorized provided that you reproduce MITRE's copyright designation and this license in any such copy."
    },
    {
      "source": "FIRST EPSS",
      "url": "https://www.first.org/epss/",
      "licence": "free to use; FIRST requests attribution"
    }
  ],
  "record": {
    "id": "CVE-2026-72642",
    "sourceURL": "https://www.cve.org/CVERecord?id=CVE-2026-72642",
    "idYear": 2026,
    "status": "PUBLISHED",
    "publishedAt": "2026-08-13T19:13:45.545Z",
    "updatedAt": "2026-08-14T03:56:07.844Z",
    "importedAt": "2026-10-24T01:58:25.636Z",
    "snapshot": {
      "id": "cve_2026-10-24_midnight_baseline",
      "at": "2026-10-24T00:00:00.000Z",
      "source": "https://github.com/CVEProject/cvelistV5/releases/tag/cve_2026-10-24_0100Z"
    },
    "facts": {
      "title": "Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process",
      "summary": "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.",
      "affected": [
        {
          "vendor": "Elastic",
          "product": "Elasticsearch",
          "defaultStatus": "unaffected",
          "versions": [
            {
              "version": "8.19.0",
              "status": "affected",
              "versionType": "semver",
              "lessThanOrEqual": "8.19.19"
            },
            {
              "version": "9.4.0",
              "status": "affected",
              "versionType": "semver",
              "lessThanOrEqual": "9.4.4"
            },
            {
              "version": "9.5.0",
              "status": "affected",
              "versionType": "semver"
            }
          ]
        }
      ]
    }
  }
}