CVE-2026-12484 (GCVE-0-2026-12484)
Vulnerability from cvelistv5 – Published: 2026-07-19 19:47 – Updated: 2026-07-20 13:45
VLAI
EPSS
VEX
Title
Unsafe Deserialization in keras.layers.TorchModuleWrapper.from_config
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.
Severity
7.8 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-20 13:45 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| keras-team | keras-team/keras |
Affected:
unspecified , ≤ latest
(custom)
|
guessed |
{
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2026-12484",
"options": [
{
"Exploitation": "poc"
},
{
"Automatable": "no"
},
{
"Technical Impact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-20T13:45:26.059176Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2026-07-20T13:45:55.809Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"references": [
{
"tags": [
"exploit"
],
"url": "https://huntr.com/bounties/ab14df49-13b5-4442-b754-3189430bfa28"
}
],
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"product": "keras-team/keras",
"vendor": "keras-team",
"versions": [
{
"lessThanOrEqual": "latest",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"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."
}
],
"metrics": [
{
"cvssV3_0": {
"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"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-502",
"description": "CWE-502 Deserialization of Untrusted Data",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-07-19T19:47:27.743Z",
"orgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
"shortName": "@huntr_ai"
},
"references": [
{
"url": "https://huntr.com/bounties/ab14df49-13b5-4442-b754-3189430bfa28"
}
],
"source": {
"advisory": "ab14df49-13b5-4442-b754-3189430bfa28",
"discovery": "EXTERNAL"
},
"title": "Unsafe Deserialization in keras.layers.TorchModuleWrapper.from_config"
}
},
"cveMetadata": {
"assignerOrgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
"assignerShortName": "@huntr_ai",
"cveId": "CVE-2026-12484",
"datePublished": "2026-07-19T19:47:27.743Z",
"dateReserved": "2026-06-17T02:52:42.641Z",
"dateUpdated": "2026-07-20T13:45:55.809Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2",
"vulnerability-lookup:meta": {
"epss": {
"cve": "CVE-2026-12484",
"date": "2026-10-27",
"epss": "0.00304",
"percentile": "0.2305"
},
"nvd": {
"cve": {
"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"
}
]
}
},
"redhat_vex": {
"aggregate_severity": "Important",
"current_release_date": "2026-09-01T20:17:12+00:00",
"cve": "CVE-2026-12484",
"id": "CVE-2026-12484",
"initial_release_date": "2026-07-19T19:47:27.743000+00:00",
"product_status:known_affected": "4",
"product_status:known_not_affected": "5",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "keras: torch: Keras: Arbitrary code execution via unsafe deserialization of PyTorch data",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-12484.json",
"version": "3"
},
"vulnrichment": {
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2026-12484",
"options": [
{
"Exploitation": "poc"
},
{
"Automatable": "no"
},
{
"Technical Impact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-20T13:45:26.059176Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2026-07-20T13:45:51.439Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"references": [
{
"tags": [
"exploit"
],
"url": "https://huntr.com/bounties/ab14df49-13b5-4442-b754-3189430bfa28"
}
],
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"product": "keras-team/keras",
"vendor": "keras-team",
"versions": [
{
"lessThanOrEqual": "latest",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"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."
}
],
"metrics": [
{
"cvssV3_0": {
"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"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-502",
"description": "CWE-502 Deserialization of Untrusted Data",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-07-19T19:47:27.743Z",
"orgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
"shortName": "@huntr_ai"
},
"references": [
{
"url": "https://huntr.com/bounties/ab14df49-13b5-4442-b754-3189430bfa28"
}
],
"source": {
"advisory": "ab14df49-13b5-4442-b754-3189430bfa28",
"discovery": "EXTERNAL"
},
"title": "Unsafe Deserialization in keras.layers.TorchModuleWrapper.from_config"
}
},
"cveMetadata": {
"assignerOrgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
"assignerShortName": "@huntr_ai",
"cveId": "CVE-2026-12484",
"datePublished": "2026-07-19T19:47:27.743Z",
"dateReserved": "2026-06-17T02:52:42.641Z",
"dateUpdated": "2026-07-20T13:45:55.809Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
Loading…
Loading…
Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
Loading timeline…
Loading…
Loading…
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.
Loading…
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.