FKIE_CVE-2026-12484
Vulnerability from fkie_nvd - Published: 2026-07-19 20:16 - Updated: 2026-10-24 20:10
Severity
Summary
A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a `safe_mode=False` parameter. When called outside a `SafeModeScope(True)` context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "keras-team/keras",
"vendor": "keras-team",
"versions": [
{
"lessThanOrEqual": "latest",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"source": "security@huntr.dev"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a `safe_mode=False` parameter. When called outside a `SafeModeScope(True)` context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode."
},
{
"lang": "es",
"value": "Una vulnerabilidad en keras-team/keras versi\u00f3n 3.15.0 permite la deserializaci\u00f3n insegura de datos pickle de PyTorch controlados por el atacante a trav\u00e9s del m\u00e9todo p\u00fablico \u2019keras.layers.TorchModuleWrapper.from_config\u2019. Este m\u00e9todo invoca \u2019torch.load(..., weights_only=False)\u2019 sin requerir una activaci\u00f3n expl\u00edcita de modo inseguro, como un par\u00e1metro \u2019safe_mode=False\u2019. Cuando se llama fuera de un contexto \u2019SafeModeScope(True)\u2019, la ausencia de un estado de modo seguro ambiental permite la deserializaci\u00f3n insegura por defecto. Este problema puede llevar a la ejecuci\u00f3n de c\u00f3digo arbitrario si se procesan configuraciones de capas de Keras no confiables utilizando este m\u00e9todo. La vulnerabilidad surge porque el m\u00e9todo no impone pr\u00e1cticas de deserializaci\u00f3n segura a menos que est\u00e9 expl\u00edcitamente protegido por el modo seguro de Keras."
}
],
"id": "CVE-2026-12484",
"lastModified": "2026-10-24T20:10:00.137",
"metrics": {
"cvssMetricV30": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.0"
},
"exploitabilityScore": 1.8,
"impactScore": 5.9,
"source": "security@huntr.dev",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-12484",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-20T13:45:26.059176Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-07-19T20:16:28.800",
"references": [
{
"source": "security@huntr.dev",
"url": "https://huntr.com/bounties/ab14df49-13b5-4442-b754-3189430bfa28"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"url": "https://huntr.com/bounties/ab14df49-13b5-4442-b754-3189430bfa28"
}
],
"sourceIdentifier": "security@huntr.dev",
"vulnStatus": "Awaiting Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-502"
}
],
"source": "security@huntr.dev",
"type": "Secondary"
}
]
}
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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.
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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
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