Vulnerability from bitnami_vulndb
Published
2026-09-03 08:40
Modified
2026-09-03 09:07
Summary
Deserialization of Untrusted Data in Elasticsearch Leading to Remote Code Execution
Details
Deserialization of Untrusted Data (CWE-502) in the Elasticsearch machine learning component can lead to remote code execution via Object Injection (CAPEC-586). A specially crafted trained model artifact could cause attacker-controlled logic to execute with a materially broader system-call surface than intended. Exploitation requires an authenticated user with sufficient privileges to create and deploy trained models.
{
"affected": [
{
"package": {
"ecosystem": "Bitnami",
"name": "elasticsearch",
"purl": "pkg:bitnami/elasticsearch"
},
"ranges": [
{
"events": [
{
"introduced": "8.0.0"
},
{
"fixed": "8.19.20"
},
{
"introduced": "9.0.0"
},
{
"fixed": "9.4.5"
},
{
"introduced": "9.5.0"
},
{
"fixed": "9.5.1"
}
],
"type": "SEMVER"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
],
"aliases": [
"CVE-2026-72649"
],
"database_specific": {
"cpes": [
"cpe:2.3:a:elastic:elasticsearch:*:*:*:*:*:*:*:*"
],
"severity": "High"
},
"details": "Deserialization of Untrusted Data (CWE-502) in the Elasticsearch machine learning component can lead to remote code execution via Object Injection (CAPEC-586). A specially crafted trained model artifact could cause attacker-controlled logic to execute with a materially broader system-call surface than intended. Exploitation requires an authenticated user with sufficient privileges to create and deploy trained models.",
"id": "BIT-elasticsearch-2026-72649",
"modified": "2026-09-03T09:07:28.402Z",
"published": "2026-09-03T08:40:41.267Z",
"references": [
{
"type": "WEB",
"url": "https://discuss.elastic.co/t/elasticsearch-8-19-20-9-4-5-9-5-1-security-update-esa-2026-114/390087"
},
{
"type": "WEB",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-72649"
}
],
"schema_version": "1.6.2",
"summary": "Deserialization of Untrusted Data in Elasticsearch Leading to Remote Code Execution"
}
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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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