{"api_version":"1","generated_at":"2026-04-14T14:26:30+00:00","cve":"CVE-2025-15379","urls":{"html":"https://cve.report/CVE-2025-15379","api":"https://cve.report/api/cve/CVE-2025-15379.json","docs":"https://cve.report/api","cve_org":"https://www.cve.org/CVERecord?id=CVE-2025-15379","nvd":"https://nvd.nist.gov/vuln/detail/CVE-2025-15379"},"summary":{"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.","state":"PUBLISHED","assigner":"@huntr_ai","published_at":"2026-03-30 08:16:15","updated_at":"2026-03-30 13:26:07"},"problem_types":["CWE-77","CWE-77 CWE-77  Improper Neutralization of Special Elements used in a Command ('Command Injection')"],"metrics":[{"version":"3.0","source":"security@huntr.dev","type":"Secondary","score":"10","severity":"CRITICAL","vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","data":{"version":"3.0","vectorString":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","baseScore":10,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"CHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"}},{"version":"3.0","source":"CNA","type":"DECLARED","score":"10","severity":"CRITICAL","vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","data":{"attackComplexity":"LOW","attackVector":"NETWORK","availabilityImpact":"HIGH","baseScore":10,"baseSeverity":"CRITICAL","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","version":"3.0"}}],"references":[{"url":"https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e","name":"https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e","refsource":"security@huntr.dev","tags":[],"title":"","mime":"","httpstatus":"","archivestatus":"0"},{"url":"https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75","name":"https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75","refsource":"security@huntr.dev","tags":[],"title":"","mime":"","httpstatus":"","archivestatus":"0"},{"url":"https://www.cve.org/CVERecord?id=CVE-2025-15379","name":"CVE Program record","refsource":"CVE.ORG","tags":["canonical"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2025-15379","name":"NVD vulnerability detail","refsource":"NVD","tags":["canonical","analysis"]}],"affected":[{"source":"CNA","vendor":"mlflow","product":"mlflow/mlflow","version":"affected unspecified 3.8.2 custom","platforms":[]}],"timeline":[],"solutions":[],"workarounds":[],"exploits":[],"credits":[],"nvd_cpes":[],"vendor_comments":[],"enrichments":{"kev":null,"epss":{"cve_year":"2025","cve_id":"15379","cve":"CVE-2025-15379","epss":"0.002370000","percentile":"0.467570000","score_date":"2026-04-13","updated_at":"2026-04-14 00:12:09"},"legacy_qids":[]},"source_records":{"cve_program":{"containers":{"adp":[{"metrics":[{"other":{"content":{"id":"CVE-2025-15379","options":[{"Exploitation":"poc"},{"Automatable":"yes"},{"Technical Impact":"total"}],"role":"CISA Coordinator","timestamp":"2026-03-31T03:55:37.623494Z","version":"2.0.3"},"type":"ssvc"}}],"providerMetadata":{"dateUpdated":"2026-03-31T13:50:57.378Z","orgId":"134c704f-9b21-4f2e-91b3-4a467353bcc0","shortName":"CISA-ADP"},"title":"CISA ADP Vulnrichment"}],"cna":{"affected":[{"product":"mlflow/mlflow","vendor":"mlflow","versions":[{"lessThan":"3.8.2","status":"affected","version":"unspecified","versionType":"custom"}]}],"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."}],"metrics":[{"cvssV3_0":{"attackComplexity":"LOW","attackVector":"NETWORK","availabilityImpact":"HIGH","baseScore":10,"baseSeverity":"CRITICAL","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","version":"3.0"}}],"problemTypes":[{"descriptions":[{"cweId":"CWE-77","description":"CWE-77  Improper Neutralization of Special Elements used in a Command ('Command Injection')","lang":"en","type":"CWE"}]}],"providerMetadata":{"dateUpdated":"2026-03-30T07:16:57.610Z","orgId":"c09c270a-b464-47c1-9133-acb35b22c19a","shortName":"@huntr_ai"},"references":[{"url":"https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75"},{"url":"https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e"}],"source":{"advisory":"dc9c1c20-7879-4050-87df-4d095fe5ca75","discovery":"EXTERNAL"},"title":"Command Injection in mlflow/mlflow"}},"cveMetadata":{"assignerOrgId":"c09c270a-b464-47c1-9133-acb35b22c19a","assignerShortName":"@huntr_ai","cveId":"CVE-2025-15379","datePublished":"2026-03-30T07:16:57.610Z","dateReserved":"2025-12-30T21:24:21.058Z","dateUpdated":"2026-03-31T13:50:57.378Z","state":"PUBLISHED"},"dataType":"CVE_RECORD","dataVersion":"5.2"},"nvd":{"publishedDate":"2026-03-30 08:16:15","lastModifiedDate":"2026-03-30 13:26:07","problem_types":["CWE-77","CWE-77 CWE-77  Improper Neutralization of Special Elements used in a Command ('Command Injection')"],"metrics":{"cvssMetricV30":[{"source":"security@huntr.dev","type":"Secondary","cvssData":{"version":"3.0","vectorString":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","baseScore":10,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"CHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":6}]},"configurations":[]},"legacy_mitre":{"record":{"CveYear":"2025","CveId":"15379","Ordinal":"1","Title":"Command Injection in mlflow/mlflow","CVE":"CVE-2025-15379","Year":"2025"},"notes":[{"CveYear":"2025","CveId":"15379","Ordinal":"1","NoteData":"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.","Type":"Description","Title":"Command Injection in mlflow/mlflow"}]}}}