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{"bugzilla": {"description": "keras: Keras: Arbitrary code execution via deserialization vulnerability", "id": "2496942", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2496942"}, "csaw": false, "cvss3": {"cvss3_base_score": "8.8", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H", "status": "draft"}, "cwe": "CWE-502", "details": ["A vulnerability in keras-team/keras version 3.14.0 allows for arbitrary code execution due to improper handling of deserialization in the `Lambda` layer. Specifically, the `_raise_for_lambda_deserialization()` function fails to enforce the safe-mode guard when `safe_mode` is set to `None`, which is the default value when `from_config()` is called outside of a `SafeModeScope` context. This logic error conflates `None` (unset/default-deny) with `False` (explicitly disabled), bypassing the guard and allowing attacker-controlled `marshal` bytecode to be deserialized. Affected call sites include `keras.layers.deserialize(config)`, `keras.models.clone_model(model)`, and any direct invocation of `Lambda.from_config(config)` without an enclosing `SafeModeScope(True)`. This vulnerability can be exploited to achieve arbitrary OS-level code execution in the context of the server or user process.", "A flaw was found in the Keras deep learning library. This vulnerability allows a remote attacker to execute arbitrary code on the system by exploiting improper handling of deserialization in the `Lambda` layer. Specifically, a security safeguard designed to prevent unsafe deserialization is bypassed when the `safe_mode` setting is not explicitly enabled, allowing malicious code to be processed. This can lead to complete compromise of the affected server or user process."], "mitigation": {"lang": "en:us", "value": "To reduce exposure, only deserialize Keras models from trusted sources. When deserializing models, explicitly enable `safe_mode` by wrapping the deserialization call within a `SafeModeScope(True)` context. This ensures the deserialization safeguard is enforced, preventing the execution of arbitrary code."}, "name": "CVE-2026-12481", "package_state": [{"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-kserve-agent-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-kserve-controller-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-kserve-router-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-kserve-storage-initializer-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-modelmesh-runtime-adapter-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "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": "Affected", "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": "Affected", "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": "Affected", "package_name": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}], "public_date": "2026-07-03T20:36:05Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-12481\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-12481\nhttps://huntr.com/bounties/59ceaed1-c8a3-4135-8f94-169ade02823d"], "statement": "This is an Important arbitrary code execution vulnerability in the Keras deep learning library, impacting Red Hat OpenShift AI components. The flaw arises from improper deserialization in the `Lambda` layer, where a security safeguard is bypassed if `safe_mode` is not explicitly enabled. Exploitation requires user interaction, such as loading a specially crafted Keras model.", "threat_severity": "Important"}

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