Vulnerability-Lookup

Vulnerability from bitnami_vulndb

Published

2026-04-29 08:45

Modified

2026-04-29 09:10

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

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{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "mlflow",
        "purl": "pkg:bitnami/mlflow"
      },
      "ranges": [
        {
          "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"
        }
      ]
    }
  ],
  "aliases": [
    "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": "2026-04-29T09:10:02.628Z",
  "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"
    }
  ],
  "schema_version": "1.6.2",
  "summary": "Command Injection in mlflow/mlflow"
}

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

Vulnerability from cvelistv5 – Published: 2026-03-30 07:16 – Updated: 2026-03-31 13:50

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

CWE

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

Assigner

@huntr_ai

References

URL

Tags

	https://huntr.com/bounties/dc9c1c20-7879-4050-87d…
	https://github.com/mlflow/mlflow/commit/361b6f620…

Impacted products

	Vendor	Product	Version
	mlflow	mlflow/mlflow	Affected: unspecified , < 3.8.2 (custom)

Show details on NVD website

JSON

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{
  "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\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."
        }
      ],
      "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 (\u0027Command Injection\u0027)",
              "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"
}

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

Vulnerability from bitnami_vulndb

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

Tags

Sightings

Nomenclature