Vulnerability-Lookup

CVE-2025-15379 (GCVE-0-2025-15379)

Vulnerability from cvelistv5 – Published: 2026-03-30 07:16 – Updated: 2026-06-30 12:07

Title

Command Injection in mlflow/mlflow

Summary

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

10 (Critical)


                        
                          CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

9 (Critical)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H

SSVC

Exploitation: poc Automatable: yes Technical Impact: total

CISA Coordinator (v2.0.3)

CWE

CWE-77 - Improper Neutralization of Special Elements used in a Command ('Command Injection')
CWE-78 - Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

Assigner

@huntr_ai

References

5 references

URL	Tags
https://huntr.com/bounties/dc9c1c20-7879-4050-87d…
https://github.com/mlflow/mlflow/commit/361b6f620…
https://access.redhat.com/security/cve/CVE-2025-15379	vdb-entryx_refsource_REDHAT
https://bugzilla.redhat.com/show_bug.cgi?id=2452949	issue-trackingx_refsource_REDHAT
https://security.access.redhat.com/data/csaf/v2/v…	x_sadp-csaf-vex

Impacted products

2 products

Vendor	Product	Version
mlflow	mlflow/mlflow	Affected: unspecified , < 3.8.2 (custom)
Red Hat	Red Hat OpenShift AI (RHOAI)	cpe:/a:redhat:openshift_ai

Show details on NVD website

JSON

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bit-mlflow-2025-15379

Vulnerability from bitnami_vulndb

Published

2026-04-29 08:45

Modified

2026-07-06 06:18

Summary

Command Injection in mlflow/mlflow

Details

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.

JSON

To clipboard

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  "affected": [
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      "package": {
        "ecosystem": "Bitnami",
        "name": "mlflow",
        "purl": "pkg:bitnami/mlflow"
      },
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        {
          "events": [
            {
              "introduced": "3.8.0"
            },
            {
              "fixed": "3.9.0"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
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    "CVE-2025-15379"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*"
    ],
    "severity": "Critical"
  },
  "details": "A command injection vulnerability exists in MLflow\u0027s 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\u0027s `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.",
  "id": "BIT-mlflow-2025-15379",
  "modified": "2026-07-06T06:18:57.499Z",
  "published": "2026-04-29T08:45:22.489Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e"
    },
    {
      "type": "WEB",
      "url": "https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-15379"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2025-15379"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2452949"
    },
    {
      "type": "WEB",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json"
    }
  ],
  "schema_version": "1.6.2",
  "summary": "Command Injection in mlflow/mlflow"
}

FKIE_CVE-2025-15379

Vulnerability from fkie_nvd - Published: 2026-03-30 08:16 - Updated: 2026-06-30 03:16

Severity

9.8 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
9.0 (Critical) - CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H

Summary

References

URL	Tags
security@huntr.dev	https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e	Patch
security@huntr.dev	https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75	Exploit, Third Party Advisory
0b0ca135-0b70-47e7-9f44-1890c2a1c46c	https://access.redhat.com/security/cve/CVE-2025-15379
0b0ca135-0b70-47e7-9f44-1890c2a1c46c	https://bugzilla.redhat.com/show_bug.cgi?id=2452949
0b0ca135-0b70-47e7-9f44-1890c2a1c46c	https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json

Impacted products

	Vendor	Product	Version
	lfprojects	mlflow	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "mlflow/mlflow",
          "vendor": "mlflow",
          "versions": [
            {
              "lessThan": "3.8.2",
              "status": "affected",
              "version": "unspecified",
              "versionType": "custom"
            }
          ]
        }
      ],
      "source": "security@huntr.dev"
    },
    {
      "affectedData": [
        {
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        }
      ],
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "CB8C7729-F7F9-4179-B66D-4E76EFE4115D",
              "versionEndIncluding": "3.8.1",
              "versionStartIncluding": "3.8.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "A command injection vulnerability exists in MLflow\u0027s 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\u0027s `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."
    }
  ],
  "id": "CVE-2025-15379",
  "lastModified": "2026-06-30T03:16:45.660",
  "metrics": {
    "cvssMetricV30": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 10.0,
          "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"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 6.0,
        "source": "security@huntr.dev",
        "type": "Secondary"
      }
    ],
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 9.8,
          "baseSeverity": "CRITICAL",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 9.0,
          "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"
        },
        "exploitabilityScore": 2.3,
        "impactScore": 6.0,
        "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2025-15379",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "yes"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-03-31T03:55:37.623494Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-03-30T08:16:15.667",
  "references": [
    {
      "source": "security@huntr.dev",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e"
    },
    {
      "source": "security@huntr.dev",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75"
    },
    {
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
      "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"
    }
  ],
  "sourceIdentifier": "security@huntr.dev",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-77"
        }
      ],
      "source": "security@huntr.dev",
      "type": "Primary"
    },
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-78"
        }
      ],
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
      "type": "Secondary"
    }
  ]
}

GHSA-R23Q-823P-VMF7

Vulnerability from github – Published: 2026-03-30 09:31 – Updated: 2026-04-01 19:08

Summary

MLflow Command Injection vulnerability

Details

Severity

10.0 (Critical)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.8.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2025-15379"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-77"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-04-01T00:08:33Z",
    "nvd_published_at": "2026-03-30T08:16:15Z",
    "severity": "CRITICAL"
  },
  "details": "A command injection vulnerability exists in MLflow\u0027s 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\u0027s `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.",
  "id": "GHSA-r23q-823p-vmf7",
  "modified": "2026-04-01T19:08:35Z",
  "published": "2026-03-30T09:31:28Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-15379"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/commit/a22ce7157f646bdce4c95106fc38ccc9ca289205"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mlflow/mlflow"
    },
    {
      "type": "WEB",
      "url": "https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "MLflow Command Injection vulnerability"
}

PYSEC-2026-423

Vulnerability from pysec - Published: 2026-06-29 11:50 - Updated: 2026-07-01 20:22

Details

Severity

10.0 (Critical)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

Impacted products

Name	purl
mlflow	pkg:pypi/mlflow

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "mlflow",
        "purl": "pkg:pypi/mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.8.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "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"
      ]
    }
  ],
  "aliases": [
    "CVE-2025-15379",
    "GHSA-r23q-823p-vmf7"
  ],
  "details": "A command injection vulnerability exists in MLflow\u0027s 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\u0027s `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.",
  "id": "PYSEC-2026-423",
  "modified": "2026-07-01T20:22:58.350388Z",
  "published": "2026-06-29T11:50:45.390530Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-15379"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/commit/a22ce7157f646bdce4c95106fc38ccc9ca289205"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mlflow/mlflow"
    },
    {
      "type": "WEB",
      "url": "https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75"
    },
    {
      "type": "PACKAGE",
      "url": "https://pypi.org/project/mlflow"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-r23q-823p-vmf7"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "MLflow Command Injection vulnerability"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

Action not permitted

CVE-2025-15379 (GCVE-0-2025-15379)

bit-mlflow-2025-15379

Vulnerability from bitnami_vulndb

FKIE_CVE-2025-15379

GHSA-R23Q-823P-VMF7

PYSEC-2026-423

Tags

Sightings

Nomenclature