CVE-2025-15379

{"id": "CVE-2025-15379", "cveTags": [], "metrics": {"ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2025-15379", "role": "CISA Coordinator", "options": [{"exploitation": "poc"}, {"automatable": "yes"}, {"technicalImpact": "total"}], "version": "2.0.3", "timestamp": "2026-03-31T03:55:37.623494Z"}}], "cvssMetricV30": [{"type": "Secondary", "source": "security@huntr.dev", "cvssData": {"scope": "CHANGED", "version": "3.0", "baseScore": 10.0, "attackVector": "NETWORK", "baseSeverity": "CRITICAL", "vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H", "integrityImpact": "HIGH", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "HIGH", "privilegesRequired": "NONE", "confidentialityImpact": "HIGH"}, "impactScore": 6.0, "exploitabilityScore": 3.9}], "cvssMetricV31": [{"type": "Primary", "source": "nvd@nist.gov", "cvssData": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 9.8, "attackVector": "NETWORK", "baseSeverity": "CRITICAL", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H", "integrityImpact": "HIGH", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "HIGH", "privilegesRequired": "NONE", "confidentialityImpact": "HIGH"}, "impactScore": 5.9, "exploitabilityScore": 3.9}, {"type": "Secondary", "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "cvssData": {"scope": "CHANGED", "version": "3.1", "baseScore": 9.0, "attackVector": "NETWORK", "baseSeverity": "CRITICAL", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H", "integrityImpact": "HIGH", "userInteraction": "REQUIRED", "attackComplexity": "LOW", "availabilityImpact": "HIGH", "privilegesRequired": "LOW", "confidentialityImpact": "HIGH"}, "impactScore": 6.0, "exploitabilityScore": 2.3}]}, "affected": [{"source": "security@huntr.dev", "affectedData": [{"vendor": "mlflow", "product": "mlflow/mlflow", "versions": [{"status": "affected", "version": "unspecified", "lessThan": "3.8.2", "versionType": "custom"}]}]}, {"source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "affectedData": [{"cpes": ["cpe:/a:redhat:openshift_ai"], "vendor": "Red Hat", "product": "Red Hat OpenShift AI (RHOAI)", "defaultStatus": "affected"}]}], "published": "2026-03-30T08:16:15.667", "references": [{"url": "https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e", "tags": ["Patch"], "source": "security@huntr.dev"}, {"url": "https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75", "tags": ["Exploit", "Third Party Advisory"], "source": "security@huntr.dev"}, {"url": "https://access.redhat.com/security/cve/CVE-2025-15379", "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"}, {"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2452949", "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"}, {"url": "https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json", "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"}], "vulnStatus": "Modified", "weaknesses": [{"type": "Primary", "source": "security@huntr.dev", "description": [{"lang": "en", "value": "CWE-77"}]}, {"type": "Secondary", "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c", "description": [{"lang": "en", "value": "CWE-78"}]}], "descriptions": [{"lang": "en", "value": "A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the `_install_model_dependencies_to_env()` function. When deploying a model with `env_manager=LOCAL`, MLflow reads dependency specifications from the model artifact's `python_env.yaml` file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2."}, {"lang": "es", "value": "Existe una vulnerabilidad de inyecci\u00f3n de comandos en el c\u00f3digo de inicializaci\u00f3n del contenedor de servicio de modelos de MLflow, espec\u00edficamente en la funci\u00f3n `_install_model_dependencies_to_env()`. Al desplegar un modelo con `env_manager=LOCAL`, MLflow lee las especificaciones de dependencia del archivo `python_env.yaml` del artefacto del modelo y las interpola directamente en un comando de shell sin sanitizaci\u00f3n. Esto permite a un atacante suministrar un artefacto de modelo malicioso y lograr la ejecuci\u00f3n arbitraria de comandos en sistemas que despliegan el modelo. La vulnerabilidad afecta a las versiones 3.8.0 y est\u00e1 corregida en la versi\u00f3n 3.8.2."}], "lastModified": "2026-06-30T03:16:45.660", "configurations": [{"nodes": [{"negate": false, "cpeMatch": [{"criteria": "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*", "vulnerable": true, "matchCriteriaId": "CB8C7729-F7F9-4179-B66D-4E76EFE4115D", "versionEndIncluding": "3.8.1", "versionStartIncluding": "3.8.0"}], "operator": "OR"}]}], "sourceIdentifier": "security@huntr.dev"}