{"dataType": "CVE_RECORD", "dataVersion": "5.2", "cveMetadata": {"cveId": "CVE-2026-12570", "assignerOrgId": "c09c270a-b464-47c1-9133-acb35b22c19a", "state": "PUBLISHED", "assignerShortName": "@huntr_ai", "dateReserved": "2026-06-18T01:55:25.311Z", "datePublished": "2026-08-10T06:29:56.111Z", "dateUpdated": "2026-08-10T17:37:39.037Z"}, "containers": {"cna": {"title": "Denial of Service via HDF5 Shape Bomb in keras.models.load_model() in keras-team/keras", "providerMetadata": {"orgId": "c09c270a-b464-47c1-9133-acb35b22c19a", "shortName": "@huntr_ai", "dateUpdated": "2026-08-10T06:29:56.111Z"}, "descriptions": [{"lang": "en", "value": "A vulnerability in keras-team/keras versions <= 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models."}], "affected": [{"vendor": "keras-team", "product": "keras-team/keras", "versions": [{"version": "unspecified", "lessThan": "3.12.3, 3.15.0", "status": "affected", "versionType": "custom"}]}], "references": [{"url": "https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8"}, {"url": "https://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756c"}], "metrics": [{"cvssV3_0": {"version": "3.0", "attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "HIGH", "confidentialityImpact": "NONE", "integrityImpact": "NONE", "privilegesRequired": "NONE", "scope": "UNCHANGED", "userInteraction": "REQUIRED", "vectorString": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H", "baseScore": 5.5, "baseSeverity": "MEDIUM"}}], "problemTypes": [{"descriptions": [{"type": "CWE", "lang": "en", "description": "CWE-770 Allocation of Resources Without Limits or Throttling", "cweId": "CWE-770"}]}], "source": {"advisory": "a064f475-780a-409a-82f7-678512f27ad8", "discovery": "EXTERNAL"}}, "adp": [{"references": [{"url": "https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8", "tags": ["exploit"]}], "metrics": [{"other": {"type": "ssvc", "content": {"timestamp": "2026-08-10T17:37:17.637683Z", "id": "CVE-2026-12570", "options": [{"Exploitation": "poc"}, {"Automatable": "no"}, {"Technical Impact": "partial"}], "role": "CISA Coordinator", "version": "2.0.3"}}}], "title": "CISA ADP Vulnrichment", "providerMetadata": {"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "shortName": "CISA-ADP", "dateUpdated": "2026-08-10T17:37:39.037Z"}}]}}

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{"bugzilla": {"description": "keras: Keras: Denial of Service via HDF5 Shape Bomb when loading malicious model files", "id": "2513026", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2513026"}, "csaw": false, "cvss3": {"cvss3_base_score": "5.0", "cvss3_scoring_vector": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:N/I:N/A:H", "status": "draft"}, "cwe": "CWE-770", "details": ["A flaw was found in Keras. When loading malicious .keras model files using the keras.models.load_model() function, the H5IOStore.__getitem__ method does not validate the shape or size of datasets. This can lead to unbounded memory allocation, allowing a malicious actor to trigger an out-of-memory condition and cause a denial of service. This vulnerability poses a risk to machine learning pipelines that process untrusted models."], "mitigation": {"lang": "en:us", "value": "Only load .keras model files from trusted sources; do not load user-supplied or externally-sourced model files without validation. Apply pod-level memory limits/requests to constrain the blast radius of an out-of-memory condition until an upstream fix is available."}, "name": "CVE-2026-12570", "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": "2026-08-10T06:29:56Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-12570\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-12570\nhttps://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756c\nhttps://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8"], "statement": "Red Hat ships Keras across multiple OpenShift AI (RHOAI) model-serving and workbench images: KServe agent, controller, router, and storage-initializer images, the ModelMesh runtime adapter image, TensorFlow pipeline-runtime images, and Jupyter TensorFlow workbench images (CUDA and ROCm variants). The keras.models.load_model() function does not validate HDF5 dataset shape or size when loading .keras model files, allowing a malicious or untrusted model file to trigger unbounded memory allocation and a denial of service (out-of-memory) on the serving/workbench pod.", "threat_severity": "Moderate"}

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