FKIE_CVE-2025-49655
Vulnerability from fkie_nvd - Published: 2025-10-17 16:15 - Updated: 2026-10-19 16:10
Severity
Summary
Deserialization of untrusted data can occur in versions of the Keras framework running versions 3.11.0 up to but not including 3.11.3, enabling a maliciously uploaded Keras file containing a TorchModuleWrapper class to run arbitrary code on an end user’s system when loaded despite safe mode being enabled. The vulnerability can be triggered through both local and remote files.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"product": "Keras",
"repo": "https://github.com/keras-team/keras",
"vendor": "Keras",
"versions": [
{
"lessThan": "3.11.3",
"status": "affected",
"version": "3.11.0",
"versionType": "semver"
}
]
}
],
"source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Deserialization of untrusted data can occur in versions of the Keras framework running versions 3.11.0 up to but not including 3.11.3, enabling a maliciously uploaded Keras file containing a\u00a0TorchModuleWrapper class to run arbitrary code on an end user\u2019s system when loaded despite safe mode being enabled. The vulnerability can be triggered through both local and remote files."
},
{
"lang": "es",
"value": "La deserializaci\u00f3n de datos no confiables puede ocurrir en versiones del framework Keras que ejecutan versiones 3.11.0 hasta la 3.11.3 (sin incluir esta \u00faltima), permitiendo que un archivo Keras cargado maliciosamente que contiene una clase \u0027TorchModuleWrapper\u0027 ejecute c\u00f3digo arbitrario en el sistema de un usuario final al cargarse, a pesar de que el modo seguro est\u00e9 habilitado. La vulnerabilidad puede activarse a trav\u00e9s de archivos tanto locales como remotos."
}
],
"id": "CVE-2025-49655",
"lastModified": "2026-10-19T16:10:00.260",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9,
"source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-49655",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "yes"
},
{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-10-17T15:57:54.529745Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-10-17T16:15:37.420",
"references": [
{
"source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
"url": "https://github.com/keras-team/keras/pull/21575"
},
{
"source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
"url": "https://hiddenlayer.com/sai_security_advisor/2025-10-keras/"
}
],
"sourceIdentifier": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-502"
}
],
"source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
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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.