{"id":"CVE-2025-15379","aliases":["BIT-mlflow-2025-15379","EUVD-2025-209121","GHSA-r23q-823p-vmf7","PYSEC-2026-423"],"status":"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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.","severity":{"version":"3.0","score":10,"vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-12","score":0.02401,"percentile":0.8427,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-77","CWE-78"],"published":"2026-03-30T07:16:57Z","modified":"2026-10-28T02:31:01Z","fixes":[{"ecosystem":"PyPI","package":"mlflow","fixed_in":["3.8.1"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"mlflow","fixed_in":["3.8.1"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"mlflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"3.8.1"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"mlflow","ranges":[{"type":"SEMVER","events":[{"introduced":"3.8.0"},{"fixed":"3.8.1"}]}]},{"source":"osv","ecosystem":"PyPI","package":"mlflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"3.8.1"}]}],"versions":["0.0.1","0.1.0","0.2.0","0.2.1","0.3.0","0.4.0","0.4.1","0.4.2","0.5.0","0.5.1","0.5.2","0.6.0","0.7.0","0.8.0","0.8.1","0.8.2","0.9.0","0.9.0.1","0.9.1","1.0.0","1.1.0","1.1.1.dev0","1.10.0","1.11.0","1.12.0","1.12.1","1.13","1.13.1","1.14.0","1.14.1","1.15.0","1.16.0","1.17.0","1.18.0","1.19.0","1.2.0","1.20.0","1.20.1","1.20.2","1.21.0","1.22.0","1.23.0","1.23.1","1.24.0","1.25.0","1.25.1","1.26.0","1.26.1","1.27.0","1.28.0","1.29.0","1.3.0","1.30.0","1.30.1","1.4.0","1.5.0","1.6.0","1.7.0","1.7.1","1.7.2","1.8.0","1.9.0","1.9.1","2.0.0","2.0.0rc0","2.0.1","2.1.0","2.1.1","2.10.0","2.10.1","2.10.2","2.11.0","2.11.1","2.11.2","2.11.3","2.11.4","2.12.0","2.12.1","2.12.2","2.13.0","2.13.1","2.13.2","2.14.0","2.14.0rc0","2.14.1","2.14.2","2.14.2.dev0","2.14.3","2.15.0","2.15.0rc0","2.15.1","2.16.0","2.16.1","2.16.2","2.17.0","2.17.0rc0","2.17.1","2.17.2","2.18.0","2.18.0rc0","2.19.0","2.19.0rc0","2.2.0","2.2.1","2.2.2","2.20.0","2.20.0rc0","2.20.1","2.20.2","2.20.3","2.20.4","2.21.0","2.21.0rc0","2.21.1","2.21.2","2.21.3","2.22.0","2.22.0rc0","2.22.1","2.22.2","2.22.3","2.22.4","2.22.5","2.3.0","2.3.1","2.3.2","2.4.0","2.4.1","2.4.2","2.5.0","2.6.0","2.7.0","2.7.1","2.8.0","2.8.1","2.9.0","2.9.1","2.9.2","3.0.0","3.0.0rc0","3.0.0rc1","3.0.0rc2","3.0.0rc3","3.0.1","3.1.0","3.1.0rc0","3.1.1","3.1.2","3.1.3","3.1.4","3.2.0","3.2.0rc0","3.3.0","3.3.0rc0","3.3.1","3.3.2","3.4.0","3.4.0rc0","3.5.0","3.5.0rc0","3.5.1","3.6.0","3.6.0rc0","3.7.0","3.7.0rc0","3.8.0","3.8.0rc0"]},{"source":"osv","ecosystem":"PyPI","package":"mlflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"3.8.1"}]}],"versions":["0.0.1","0.1.0","0.2.0","0.2.1","0.3.0","0.4.0","0.4.1","0.4.2","0.5.0","0.5.1","0.5.2","0.6.0","0.7.0","0.8.0","0.8.1","0.8.2","0.9.0","0.9.0.1","0.9.1","1.0.0","1.1.0","1.1.1.dev0","1.10.0","1.11.0","1.12.0","1.12.1","1.13","1.13.1","1.14.0","1.14.1","1.15.0","1.16.0","1.17.0","1.18.0","1.19.0","1.2.0","1.20.0","1.20.1","1.20.2","1.21.0","1.22.0","1.23.0","1.23.1","1.24.0","1.25.0","1.25.1","1.26.0","1.26.1","1.27.0","1.28.0","1.29.0","1.3.0","1.30.0","1.30.1","1.4.0","1.5.0","1.6.0","1.7.0","1.7.1","1.7.2","1.8.0","1.9.0","1.9.1","2.0.0","2.0.0rc0","2.0.1","2.1.0","2.1.1","2.10.0","2.10.1","2.10.2","2.11.0","2.11.1","2.11.2","2.11.3","2.11.4","2.12.0","2.12.1","2.12.2","2.13.0","2.13.1","2.13.2","2.14.0","2.14.0rc0","2.14.1","2.14.2","2.14.2.dev0","2.14.3","2.15.0","2.15.0rc0","2.15.1","2.16.0","2.16.1","2.16.2","2.17.0","2.17.0rc0","2.17.1","2.17.2","2.18.0","2.18.0rc0","2.19.0","2.19.0rc0","2.2.0","2.2.1","2.2.2","2.20.0","2.20.0rc0","2.20.1","2.20.2","2.20.3","2.20.4","2.21.0","2.21.0rc0","2.21.1","2.21.2","2.21.3","2.22.0","2.22.0rc0","2.22.1","2.22.2","2.22.3","2.22.4","2.22.5","2.3.0","2.3.1","2.3.2","2.4.0","2.4.1","2.4.2","2.5.0","2.6.0","2.7.0","2.7.1","2.8.0","2.8.1","2.9.0","2.9.1","2.9.2","3.0.0","3.0.0rc0","3.0.0rc1","3.0.0rc2","3.0.0rc3","3.0.1","3.1.0","3.1.0rc0","3.1.1","3.1.2","3.1.3","3.1.4","3.2.0","3.2.0rc0","3.3.0","3.3.0rc0","3.3.1","3.3.2","3.4.0","3.4.0rc0","3.5.0","3.5.0rc0","3.5.1","3.6.0","3.6.0rc0","3.7.0","3.7.0rc0","3.8.0","3.8.0rc0"]}],"products":[{"vendor":"lfprojects","product":"mlflow"},{"vendor":"mlflow","product":"mlflow/mlflow"},{"vendor":"red hat","product":"red hat openshift ai (rhoai)"}],"references":[{"url":"https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75","tags":["Exploit","Third Party Advisory","advisory","web"]},{"url":"https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e","tags":["Patch","fix","web"]},{"url":"https://access.redhat.com/security/cve/CVE-2025-15379","tags":["vdb-entry","x_refsource_REDHAT","Third Party Advisory","advisory"]},{"url":"https://bugzilla.redhat.com/show_bug.cgi?id=2452949","tags":["issue-tracking","x_refsource_REDHAT","Third Party Advisory","advisory","report"]},{"url":"https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json","tags":["x_sadp-csaf-vex","Third Party Advisory","advisory","web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2025-15379","tags":["web","advisory"]},{"url":"https://github.com/mlflow/mlflow/commit/a22ce7157f646bdce4c95106fc38ccc9ca289205","tags":["web"]},{"url":"https://github.com/advisories/GHSA-r23q-823p-vmf7","tags":["advisory"]},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2025/15xxx/CVE-2025-15379.json","tags":["advisory"]},{"url":"https://github.com/mlflow/mlflow","tags":["package"]},{"url":"https://pypi.org/project/mlflow","tags":["package"]}],"sources":{"cve":[{"id":"CVE-2025-15379","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.","status":"published","published":"2026-03-30T07:16:57.61Z","modified":"2026-09-07T12:05:10.048Z","cvss":[{"version":"3.0","score":10,"vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical","assessor":"cna"},{"version":"3.1","score":9,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H","level":"critical","assessor":"adp"}],"cwes":["CWE-77","CWE-78"],"updated_at":"2026-11-02T14:32:37.351404Z"}],"euvd":[{"id":"EUVD-2025-209121","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.","status":"published","published":"2026-03-30T07:16:57Z","modified":"2026-09-07T12:05:10Z","cvss":[{"version":"3.1","score":9,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H","level":"critical"}],"updated_at":"2026-11-02T17:28:30.668645Z"}],"ghsa":[{"id":"GHSA-r23q-823p-vmf7","title":"MLflow Command Injection vulnerability","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.1.","status":"published","published":"2026-03-30T09:31:28Z","modified":"2026-04-01T19:08:35Z","cvss":[{"version":"3.0","score":10,"vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical"}],"cwes":["CWE-77"],"updated_at":"2026-11-02T14:39:47.075804Z"}],"nvd":[{"id":"CVE-2025-15379","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.","status":"published","published":"2026-03-30T08:16:15.667Z","modified":"2026-09-04T20:02:09.23Z","cvss":[{"version":"3.1","score":10,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical","assessor":"primary"},{"version":"3.1","score":9,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H","level":"critical","assessor":"secondary"},{"version":"3.0","score":10,"vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical","assessor":"secondary"}],"cwes":["CWE-77","CWE-78"],"updated_at":"2026-11-02T14:12:01.641503Z"}],"osv":[{"id":"CVE-2025-15379","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.","status":"published","published":"2026-03-30T07:16:57.61Z","modified":"2026-10-28T02:31:01.933698182Z","cvss":[{"version":"3.1","score":9,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H","level":"critical"}],"cwes":["CWE-77"],"updated_at":"2026-11-02T15:44:46.340268Z"},{"id":"GHSA-r23q-823p-vmf7","title":"MLflow Command Injection vulnerability","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.1.","status":"published","published":"2026-03-30T09:31:28Z","modified":"2026-09-10T03:50:40.631796803Z","cvss":[{"version":"3.0","score":10,"vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical"}],"cwes":["CWE-77"],"updated_at":"2026-11-02T15:46:55.122811Z"},{"id":"PYSEC-2026-423","title":"MLflow Command Injection vulnerability","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.1.","status":"published","published":"2026-06-29T11:50:45.39053Z","modified":"2026-07-01T20:22:58.350388Z","cvss":[{"version":"3.0","score":10,"vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","level":"critical"}],"updated_at":"2026-11-02T15:52:57.671918Z"},{"id":"BIT-mlflow-2025-15379","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. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.","status":"published","published":"2026-04-29T08:45:22.489Z","modified":"2026-09-10T16:01:43.067195422Z","updated_at":"2026-11-02T15:28:33.072289Z"}]}}