CVE-2026-4035

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"versionEndExcluding": "3.11.0", "vulnerable": true}], "negate": false, "operator": "OR"}]}], "cveTags": [], "descriptions": [{"lang": "en", "value": "A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0."}, {"lang": "es", "value": "Una vulnerabilidad en las versiones de mlflow/mlflow anteriores a la 3.11.0 permite la resoluci\u00f3n de variables de entorno en secretos de AI Gateway, lo que puede ser explotado para exfiltrar credenciales de entorno sensibles del lado del servidor a un punto final controlado por un atacante. Este problema surge porque el campo 'api_key' en los secretos del gateway puede aceptar referencias a '$ENV_VAR', que se resuelven contra el entorno del servidor de MLflow durante el tiempo de ejecuci\u00f3n. Los secretos resueltos se env\u00edan entonces en los encabezados de autenticaci\u00f3n del proveedor al 'api_base' ascendente configurado. Esta vulnerabilidad puede ser explotada por usuarios autenticados con bajos privilegios en implementaciones con 'basic-auth' o por usuarios no autenticados en implementaciones predeterminadas sin 'basic-auth'. El impacto incluye la posible fuga de credenciales sensibles, como credenciales de artefactos en la nube ('AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY'), lo que podr\u00eda llevar al envenenamiento de artefactos y a la ejecuci\u00f3n de c\u00f3digo transfronteriza en entornos descendentes. El problema est\u00e1 solucionado en la versi\u00f3n 3.11.0."}], "id": "CVE-2026-4035", "lastModified": "2026-08-14T13:19:00.193", "metrics": {"cvssMetricV30": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 9.1, "baseSeverity": "CRITICAL", "confidentialityImpact": "HIGH", "integrityImpact": "LOW", "privilegesRequired": "LOW", "scope": "CHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:L", "version": "3.0"}, "exploitabilityScore": 3.1, "impactScore": 5.3, "source": "security@huntr.dev", "type": "Secondary"}], "cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "NONE", "baseScore": 7.7, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "NONE", "privilegesRequired": "LOW", "scope": "CHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N", "version": "3.1"}, "exploitabilityScore": 3.1, "impactScore": 4.0, "source": "nvd@nist.gov", "type": "Primary"}, {"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "NONE", "baseScore": 7.7, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "NONE", "privilegesRequired": "LOW", "scope": "CHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N", "version": "3.1"}, "exploitabilityScore": 3.1, "impactScore": 4.0, "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "type": "Secondary"}], "ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2026-4035", "options": [{"exploitation": "poc"}, {"automatable": "no"}, {"technicalImpact": "partial"}], "role": "CISA Coordinator", "timestamp": "2026-06-03T13:10:20.303201Z", "version": "2.0.3"}}]}, "published": "2026-06-03T09:16:13.083", "references": [{"source": "security@huntr.dev", "tags": ["Patch"], "url": "https://github.com/mlflow/mlflow/commit/4a3f2f720cb4f058c9e0c5b883e0acc9ab64a7f3"}, {"source": "security@huntr.dev", "tags": ["Exploit", "Third Party Advisory"], "url": "https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"}, {"source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "url": "https://access.redhat.com/security/cve/CVE-2026-4035"}, {"source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2484318"}, {"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "tags": ["Exploit", "Third Party Advisory"], "url": "https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"}, {"source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-4035.json"}], "sourceIdentifier": "security@huntr.dev", "vulnStatus": "Modified", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-201"}], "source": "security@huntr.dev", "type": "Secondary"}, {"description": [{"lang": "en", "value": "CWE-201"}], "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "type": "Secondary"}]}

{"bugzilla": {"description": "python-mlflow: MLflow: Sensitive credential exfiltration via environment variable resolution in AI Gateway secrets", "id": "2484318", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2484318"}, "csaw": false, "cvss3": {"cvss3_base_score": "7.7", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N", "status": "draft"}, "cwe": "CWE-201", "details": ["A flaw was found in MLflow. This vulnerability allows an attacker to exfiltrate sensitive server-side environment credentials. It occurs because the AI Gateway secrets can resolve environment variables, which are then sent to an attacker-controlled endpoint. This could lead to unauthorized access to cloud resources and potentially enable cross-boundary code execution."], "mitigation": {"lang": "en:us", "value": "To mitigate this issue, restrict network access to the MLflow server to trusted clients only. Additionally, ensure that authentication mechanisms, such as basic-auth, are properly configured and enabled for MLflow deployments to prevent unauthenticated or low-privileged access to the AI Gateway. Consult MLflow documentation for specific configuration steps related to network access control and authentication. A restart or reload of the MLflow service may be required for changes to take effect."}, "name": "CVE-2026-4035", "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-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": "2026-06-03T07:18:08Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-4035\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-4035\nhttps://github.com/mlflow/mlflow/commit/4a3f2f720cb4f058c9e0c5b883e0acc9ab64a7f3\nhttps://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"], "statement": "This is a flaw in MLflow Server\u2019s AI Gateway: if a gateway secret\u2019s api_key uses a $ENV_VAR form, the server resolves it from pod environment variables and can send those values to a configured upstream api_base. The primary impact is credential disclosure (CWE-201), not direct remote code execution on the platform.\nFor OpenShift AI, most affected Konflux images embed the mlflow package (notebooks, pipelines, training runtimes) but do not run MLflow Server + AI Gateway in their default role. The clearest product exposure is odh-mlflow-rhel9, where the server component is actually shipped and operated. Exploitation requires Gateway configuration reachability and, in typical deployments, authenticated access (PR:L); unauthenticated abuse applies only where MLflow is deployed without basic-auth. Downstream artifact abuse depends on secondary use of leaked credentials, not a standalone RCE primitive in OpenShift AI itself. Hence, the impact is set to Important.", "threat_severity": "Important"}

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