CVE-2026-12480

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{"affected": [{"affectedData": [{"product": "keras-team/keras", "vendor": "keras-team", "versions": [{"lessThan": "3.12.2, 3.14.1", "status": "affected", "version": "unspecified", "versionType": "custom"}]}], "source": "security@huntr.dev"}], "cveTags": [], "descriptions": [{"lang": "en", "value": "Keras versions up to and including 3.13.2 are vulnerable to an arbitrary HDF5 file read due to an incomplete fix for CVE-2026-1669. The vulnerability resides in the `H5IOStore._verify_dataset()` and `file_editor.py` methods, which fail to check the `dataset.is_virtual` property of HDF5 datasets. This allows an attacker to craft a malicious `.keras` model archive or `.h5` weights file containing a Virtual Dataset (VDS) that references external HDF5 files on the victim's filesystem. When the victim loads the model using `keras.models.load_model()` or `keras.saving.load_model()`, the external file is transparently read, leading to potential information disclosure. Fixed in versions 3.12.2 and 3.14.1."}, {"lang": "es", "value": "Las versiones de Keras hasta la 3.13.2 inclusive son vulnerables a una lectura arbitraria de archivos HDF5 debido a una correcci\u00f3n incompleta para CVE-2026-1669. La vulnerabilidad reside en los m\u00e9todos \u201cH5IOStore._verify_dataset()\u201d y \u201cfile_editor.py\u201d, que no verifican la propiedad \u201cdataset.is_virtual\u201d de los conjuntos de datos HDF5. Esto permite a un atacante crear un archivo de modelo .keras malicioso o un archivo de pesos .h5 que contiene un Conjunto de Datos Virtual (VDS) que hace referencia a archivos HDF5 externos en el sistema de archivos de la v\u00edctima. Cuando la v\u00edctima carga el modelo usando \u201ckeras.models.load_model()\u201d o \u201ckeras.saving.load_model()\u201d, el archivo externo se lee de forma transparente, lo que lleva a una potencial revelaci\u00f3n de informaci\u00f3n. Corregido en las versiones 3.12.2 y 3.14.1."}], "id": "CVE-2026-12480", "lastModified": "2026-10-25T17:10:00.197", "metrics": {"cvssMetricV30": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "NONE", "baseScore": 5.5, "baseSeverity": "MEDIUM", "confidentialityImpact": "HIGH", "integrityImpact": "NONE", "privilegesRequired": "NONE", "scope": "UNCHANGED", "userInteraction": "REQUIRED", "vectorString": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N", "version": "3.0"}, "exploitabilityScore": 1.8, "impactScore": 3.6, "source": "security@huntr.dev", "type": "Secondary"}], "ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2026-12480", "options": [{"exploitation": "poc"}, {"automatable": "no"}, {"technicalImpact": "partial"}], "role": "CISA Coordinator", "timestamp": "2026-07-01T17:46:27.752463Z", "version": "2.0.3"}}]}, "published": "2026-07-01T17:16:19.330", "references": [{"source": "security@huntr.dev", "url": "https://github.com/keras-team/keras/commit/d5a88bdb137c0d3039b8f4bbbe8c7099925cc10c"}, {"source": "security@huntr.dev", "url": "https://huntr.com/bounties/1875d257-5b03-4a69-ac70-e98653fa12c7"}, {"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "url": "https://huntr.com/bounties/1875d257-5b03-4a69-ac70-e98653fa12c7"}], "sourceIdentifier": "security@huntr.dev", "vulnStatus": "Awaiting Analysis", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-73"}], "source": "security@huntr.dev", "type": "Secondary"}]}

{"bugzilla": {"description": "keras: Keras: Information disclosure via malicious model archive with Virtual Dataset", "id": "2496097", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2496097"}, "csaw": false, "cvss3": {"cvss3_base_score": "5.5", "cvss3_scoring_vector": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N", "status": "draft"}, "cwe": "CWE-22", "details": ["Keras versions up to and including 3.13.2 are vulnerable to an arbitrary HDF5 file read due to an incomplete fix for CVE-2026-1669. The vulnerability resides in the `H5IOStore._verify_dataset()` and `file_editor.py` methods, which fail to check the `dataset.is_virtual` property of HDF5 datasets. This allows an attacker to craft a malicious `.keras` model archive or `.h5` weights file containing a Virtual Dataset (VDS) that references external HDF5 files on the victim's filesystem. When the victim loads the model using `keras.models.load_model()` or `keras.saving.load_model()`, the external file is transparently read, leading to potential information disclosure. Fixed in versions 3.12.2 and 3.14.1.", "A flaw was found in Keras. An attacker can craft a malicious model archive or weights file containing a Virtual Dataset (VDS) that references external files on a victim's system. When a user loads this malicious model, the external file is transparently read. This vulnerability leads to information disclosure, allowing an attacker to access sensitive data from the victim's filesystem."], "name": "CVE-2026-12480", "package_state": [{"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-agent-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-controller-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-router-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-storage-initializer-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-modelmesh-runtime-adapter-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}], "public_date": "2026-07-01T16:53:32Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-12480\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-12480\nhttps://github.com/keras-team/keras/commit/d5a88bdb137c0d3039b8f4bbbe8c7099925cc10c\nhttps://huntr.com/bounties/1875d257-5b03-4a69-ac70-e98653fa12c7"], "statement": "Moderate: This information disclosure flaw in Keras affects Red Hat OpenShift AI components that utilize Keras. The vulnerability allows an attacker to read arbitrary files on the victim's system if a malicious Keras model archive or weights file containing a Virtual Dataset (VDS) is loaded. Exploitation requires user interaction, specifically loading an untrusted model, which limits the attack vector to scenarios where users process external, potentially malicious, model files.", "threat_severity": "Moderate"}

{"affected": [{"configurations": [{"platform": null, "status": "affected", "versions": {"scheme": "semver", "value": "[0,3.14.1)"}}], "enrichment": {"confidence": 100.0, "confidence_source": "manual", "scores": [{"score": 100.0, "source": "manual"}]}, "original": {"product": "keras-team/keras", "source": "cna", "vendor": "keras-team"}, "product": "keras", "vendor": "keras-team"}], "created": "2026-07-01T23:00:05.531957+00:00", "updated": "2026-08-03T06:15:04.017285+00:00", "vendors": ["keras-team", "keras-team$PRODUCT$keras"]}