{"id":"CVE-2025-15379","state":"PUBLISHED","title":"Command Injection in mlflow/mlflow","description":"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. 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The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2."}],"affected":[{"vendor":"mlflow","product":"mlflow/mlflow","versions":[{"version":"unspecified","status":"affected","lessThan":"3.8.2","versionType":"custom"}]}],"references":[{"url":"https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75"},{"url":"https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e"}],"metrics":[{"cvssV3_0":{"version":"3.0","attackComplexity":"LOW","attackVector":"NETWORK","availabilityImpact":"HIGH","confidentialityImpact":"HIGH","integrityImpact":"HIGH","privilegesRequired":"NONE","scope":"CHANGED","userInteraction":"NONE","vectorString":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","baseScore":10,"baseSeverity":"CRITICAL"}}],"problemTypes":[{"descriptions":[{"cweId":"CWE-77","type":"CWE","lang":"en","description":"CWE-77  Improper Neutralization of Special Elements used in a Command ('Command Injection')"}]}],"providerMetadata":{"shortName":"@huntr_ai","orgId":"c09c270a-b464-47c1-9133-acb35b22c19a","dateUpdated":"2026-03-30T07:16:57.610Z"},"source":{"advisory":"dc9c1c20-7879-4050-87df-4d095fe5ca75","discovery":"EXTERNAL"}},"adp":[{"title":"CISA ADP Vulnrichment","metrics":[{"other":{"type":"ssvc","content":{"timestamp":"2026-03-31T03:55:37.623494Z","id":"CVE-2025-15379","options":[{"Exploitation":"poc"},{"Automatable":"yes"},{"Technical Impact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}}],"providerMetadata":{"shortName":"CISA-ADP","orgId":"134c704f-9b21-4f2e-91b3-4a467353bcc0","dateUpdated":"2026-03-31T13:50:57.378Z"}},{"title":"mlflow: MLflow: Arbitrary command execution via command injection in model serving container initialization.","descriptions":[{"lang":"en","value":"A flaw was found in MLflow. When deploying a model with `env_manager=LOCAL`, MLflow's model serving container initialization code, specifically the `_install_model_dependencies_to_env()` function, reads dependency specifications from the model artifact's `python_env.yaml` file. An attacker can supply a malicious model artifact, leading to command injection as these specifications are directly interpolated into a shell command without proper sanitization. This allows for arbitrary command execution on systems deploying the malicious model."}],"affected":[{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","packageName":"rhoai/odh-mlflow-rhel9","defaultStatus":"unaffected","collectionURL":"https://access.redhat.com/downloads/content/package-browser/","cpes":["cpe:/a:redhat:openshift_ai"]},{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","packageName":"rhoai/odh-training-cuda128-torch29-py312-rhel9","defaultStatus":"affected","collectionURL":"https://access.redhat.com/downloads/content/package-browser/","cpes":["cpe:/a:redhat:openshift_ai"]}],"references":[{"url":"https://access.redhat.com/security/cve/CVE-2025-15379","tags":["vdb-entry","x_refsource_REDHAT"]},{"url":"https://bugzilla.redhat.com/show_bug.cgi?id=2452949","name":"RHBZ#2452949","tags":["issue-tracking","x_refsource_REDHAT"]},{"url":"https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json","tags":["x_sadp-csaf-vex"]}],"metrics":[{"other":{"content":{"namespace":"https://access.redhat.com/security/updates/classification/","value":"Important"},"type":"Red Hat severity rating"}},{"cvssV3_1":{"attackComplexity":"LOW","attackVector":"NETWORK","availabilityImpact":"HIGH","baseScore":9,"baseSeverity":"CRITICAL","confidentialityImpact":"HIGH","integrityImpact":"HIGH","privilegesRequired":"LOW","scope":"CHANGED","userInteraction":"REQUIRED","vectorString":"CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H","version":"3.1"},"format":"CVSS"}],"problemTypes":[{"descriptions":[{"cweId":"CWE-78","description":"Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')","lang":"en","type":"CWE"}]}],"providerMetadata":{"shortName":"redhat-SADP","orgId":"0b0ca135-0b70-47e7-9f44-1890c2a1c46c","dateUpdated":"2026-09-07T12:05:10.048Z"},"datePublic":"2026-03-30T07:16:57.610Z","timeline":[{"lang":"en","time":"2026-03-30T08:01:15.603Z","value":"Reported to Red Hat."},{"lang":"en","time":"2026-03-30T07:16:57.610Z","value":"Made public."}],"workarounds":[{"lang":"en","value":"To mitigate this issue, avoid deploying MLflow models with `env_manager=LOCAL`. If using `env_manager=LOCAL` is unavoidable, ensure that all model artifacts, particularly their `python_env.yaml` files, originate from trusted sources and are thoroughly vetted for malicious content. This operational control helps prevent the injection of arbitrary commands during model serving container initialization."}],"x_adpType":"supplier","x_generator":{"engine":"sadp-cli 1.0.0"}}]}}}