FKIE_CVE-2026-0897
Vulnerability from fkie_nvd - Published: 2026-01-15 14:16 - Updated: 2026-07-15 02:18
Severity
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.6 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:H
7.6 (High) - CVSS:3.1/
Summary
Allocation of Resources Without Limits or Throttling in the HDF5 weight loading component in Google Keras 3.0.0 through 3.13.0 on all platforms allows a remote attacker to cause a Denial of Service (DoS) through memory exhaustion and a crash of the Python interpreter via a crafted .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape.
References
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"versionEndIncluding": "3.13.0",
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Allocation of Resources Without Limits or Throttling in the HDF5 weight loading component\u00a0in Google\u00a0Keras\u00a03.0.0 through 3.13.0\u00a0on all platforms\u00a0allows a remote attacker\u00a0to cause a Denial of Service (DoS) through memory exhaustion and a crash of the Python interpreter\u00a0via a crafted .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape."
},
{
"lang": "es",
"value": "Asignaci\u00f3n de recursos sin l\u00edmites ni estrangulamiento en el componente de carga de pesos HDF5 en Google Keras 3.0.0 hasta 3.13.0 en todas las plataformas permite a un atacante remoto causar una denegaci\u00f3n de servicio (DoS) a trav\u00e9s del agotamiento de la memoria y un fallo del int\u00e9rprete de Python mediante un archivo .keras manipulado que contiene un archivo model.weights.h5 v\u00e1lido cuyo conjunto de datos declara una forma extremadamente grande."
}
],
"id": "CVE-2026-0897",
"lastModified": "2026-07-15T02:18:00.310",
"metrics": {
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
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"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
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"userInteraction": "PASSIVE",
"valueDensity": "NOT_DEFINED",
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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.
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.
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