CVE-2026-96804

{"dataType": "CVE_RECORD", "dataVersion": "5.2", "cveMetadata": {"cveId": "CVE-2026-96804", "assignerOrgId": "37e5125f-f79b-445b-8fad-9564f167944b", "state": "PUBLISHED", "assignerShortName": "certcc", "dateReserved": "2026-09-23T16:31:15.586Z", "datePublished": "2026-09-23T16:35:05.250Z", "dateUpdated": "2026-09-24T03:55:42.990Z"}, "containers": {"cna": {"title": "CVE-2026-96804", "descriptions": [{"lang": "en", "value": "MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact."}], "source": {"discovery": "UNKNOWN"}, "affected": [{"vendor": "MLflow", "product": "MLflow", "versions": [{"status": "affected", "version": "2.1", "lessThan": "3.15.0", "versionType": "custom"}]}], "problemTypes": [{"descriptions": [{"lang": "en", "description": "CWE-502 Deserialization of Untrusted Data"}]}], "references": [{"url": "https://kb.cert.org/vuls/id/369093"}], "x_generator": {"engine": "VINCE 3.0.49", "env": "prod", "origin": "https://cveawg.mitre.org/api/cve/CVE-2026-96804"}, "providerMetadata": {"orgId": "37e5125f-f79b-445b-8fad-9564f167944b", "shortName": "certcc", "dateUpdated": "2026-09-23T16:35:05.250Z"}}, "adp": [{"problemTypes": [{"descriptions": [{"type": "CWE", "cweId": "CWE-502", "lang": "en", "description": "CWE-502 Deserialization of Untrusted Data"}]}], "metrics": [{"cvssV3_1": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 8.8, "attackVector": "NETWORK", "baseSeverity": "HIGH", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H", "integrityImpact": "HIGH", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "HIGH", "privilegesRequired": "LOW", "confidentialityImpact": "HIGH"}}, {"other": {"type": "ssvc", "content": {"timestamp": "2026-09-23T00:00:00+00:00", "options": [{"Exploitation": "none"}, {"Automatable": "no"}, {"Technical Impact": "total"}], "role": "CISA Coordinator", "version": "2.0.3", "id": "CVE-2026-96804"}}}], "title": "CISA ADP Vulnrichment", "providerMetadata": {"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "shortName": "CISA-ADP", "dateUpdated": "2026-09-24T03:55:42.990Z"}}]}}

{"dataType": "CVE_RECORD", "containers": {"adp": [{"title": "CISA ADP Vulnrichment", "metrics": [{"cvssV3_1": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 8.8, "attackVector": "NETWORK", "baseSeverity": "HIGH", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H", "integrityImpact": "HIGH", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "HIGH", "privilegesRequired": "LOW", "confidentialityImpact": "HIGH"}}, {"other": {"type": "ssvc", "content": {"id": "CVE-2026-96804", "role": "CISA Coordinator", "options": [{"Exploitation": "none"}, {"Automatable": "no"}, {"Technical Impact": "total"}], "version": "2.0.3", "timestamp": "2026-09-23T17:00:12.740363Z"}}}], "problemTypes": [{"descriptions": [{"lang": "en", "type": "CWE", "cweId": "CWE-502", "description": "CWE-502 Deserialization of Untrusted Data"}]}], "providerMetadata": {"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "shortName": "CISA-ADP", "dateUpdated": "2026-09-23T17:00:25.510Z"}}], "cna": {"title": "CVE-2026-96804", "source": {"discovery": "UNKNOWN"}, "affected": [{"vendor": "MLflow", "product": "MLflow", "versions": [{"status": "affected", "version": "2.1", "lessThan": "3.15.0", "versionType": "custom"}]}], "references": [{"url": "https://kb.cert.org/vuls/id/369093"}], "x_generator": {"env": "prod", "engine": "VINCE 3.0.49", "origin": "https://cveawg.mitre.org/api/cve/CVE-2026-96804"}, "descriptions": [{"lang": "en", "value": "MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact."}], "problemTypes": [{"descriptions": [{"lang": "en", "description": "CWE-502 Deserialization of Untrusted Data"}]}], "providerMetadata": {"orgId": "37e5125f-f79b-445b-8fad-9564f167944b", "shortName": "certcc", "dateUpdated": "2026-09-23T16:35:05.250Z"}}}, "cveMetadata": {"cveId": "CVE-2026-96804", "state": "PUBLISHED", "dateUpdated": "2026-09-24T03:55:42.990Z", "dateReserved": "2026-09-23T16:31:15.586Z", "assignerOrgId": "37e5125f-f79b-445b-8fad-9564f167944b", "datePublished": "2026-09-23T16:35:05.250Z", "assignerShortName": "certcc"}, "dataVersion": "5.2"}

{"affected": [{"affectedData": [{"product": "MLflow", "vendor": "MLflow", "versions": [{"lessThan": "3.15.0", "status": "affected", "version": "2.1", "versionType": "custom"}]}], "source": "cret@cert.org"}], "cveTags": [], "descriptions": [{"lang": "en", "value": "MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact."}], "id": "CVE-2026-96804", "lastModified": "2026-09-24T04:18:04.600", "metrics": {"cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "HIGH", "baseScore": 8.8, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H", "version": "3.1"}, "exploitabilityScore": 2.8, "impactScore": 5.9, "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "type": "Secondary"}], "ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2026-96804", "options": [{"exploitation": "none"}, {"automatable": "no"}, {"technicalImpact": "total"}], "role": "CISA Coordinator", "timestamp": "2026-09-23T00:00:00+00:00", "version": "2.0.3"}}]}, "published": "2026-09-23T17:17:25.530", "references": [{"source": "cret@cert.org", "url": "https://kb.cert.org/vuls/id/369093"}], "sourceIdentifier": "cret@cert.org", "vulnStatus": "Deferred", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-502"}], "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "type": "Secondary"}]}

{"bugzilla": {"description": "mlflow: MLflow: Arbitrary Code Execution via Insecure Deserialization", "id": "2539535", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2539535"}, "csaw": false, "cvss3": {"cvss3_base_score": "8.8", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H", "status": "draft"}, "cwe": "CWE-502", "details": ["MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.", "A flaw was found in MLflow. The statsmodel component of MLflow fails to enforce a security control that prevents insecure deserialization during model loading. This allows a remote attacker to execute arbitrary code by submitting a specially crafted MLmodel artifact. This vulnerability can lead to a complete compromise of the affected system."], "mitigation": {"lang": "en:us", "value": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability."}, "name": "CVE-2026-96804", "package_state": [{"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-mlflow-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-pytorch-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-pytorch-llmcompressor-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-pytorch-rocm-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-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-th06-cpu-torch210-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-th06-cuda130-torch210-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-th06-rocm64-torch291-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-th-torch-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-th-torch-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-th-torch-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-training-cuda128-torch29-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-pytorch-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-pytorch-llmcompressor-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-pytorch-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)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}], "public_date": "2026-09-23T16:35:05Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-96804\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-96804\nhttps://kb.cert.org/vuls/id/369093"], "statement": "This vulnerability is rated as Important rather than Moderate because successful exploitation permits arbitrary code execution within the container workload, though it requires an attacker to supply a malicious model artifact. In Red Hat OpenShift AI environments, MLflow is deployed across workbench images and automated pipelines to manage machine learning lifecycles. The security risk is realized only when a user or automated job processes an untrusted statsmodels artifact, constraining the impact to the privileges of the executing container.", "threat_severity": "Important"}

{"affected": [{"configurations": [{"platform": null, "status": "affected", "versions": {"scheme": "generic", "value": "[2.1,3.15.0)"}}], "enrichment": {"confidence": 100.0, "confidence_source": "matching", "scores": [{"score": 100.0, "source": "matching"}]}, "original": {"product": "MLflow", "source": "cna", "vendor": "MLflow"}, "product": "mlflow", "vendor": "mlflow"}], "created": "2026-09-23T18:30:06.967660+00:00", "title": "Remote Code Execution via Deserialization of Crafted MLmodel Artifact in MLflow Statsmodel Flavor", "updated": "2026-09-23T19:00:07.822149+00:00", "vendors": ["mlflow", "mlflow$PRODUCT$mlflow"]}