MLflow Command Injection vulnerability

Exploitation

No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.

CVSS

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

EPSS

• CVE-2025-15379: 2.40% estimated exploitation activity in the next 30 days; percentile 84.05 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.

As published by the source; long descriptions are shortened.

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv() function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.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.

Published by the source

30 Mar 2026, 09:31 UTC

Last modified by the source

10 Sep 2026, 03:50 UTC

Source last checked

26 Oct 2026, 13:40 UTC

1. Revision 3 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-r23q-823p-vmf7

2. Revision 2 · amended · 24 Oct 2026

   Amended from osv record GHSA-r23q-823p-vmf7

3. Revision 1 · created · 23 Oct 2026

   Published from ghsa record GHSA-r23q-823p-vmf7

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# Patch Next advisory brief: CVE-2025-15379

Advisory: MLflow Command Injection vulnerability
IDs: CVE-2025-15379, GHSA-r23q-823p-vmf7
Record: https://patchnext.com/advisories/cve-2025-15379 (revision 3, source checked 26 Oct 2026, 13:40 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: Critical (CVSS 10.0)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: mlflow 3.8.1. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI mlflow: affected < 3.8.1; fixed in 3.8.1

## Suggested inspection (read-only)
- `python -m pip show mlflow`: prints the installed version. Run with the environment that serves production active.

## Supported action
Upgrade each affected component to a fixed version on its release line. For a transitive dependency, upgrade the parent package or add an override or constraint, then regenerate the lockfile. Redeploy every environment that ran an affected version.

## Required regression checks
- Run the test suite and a production build.
- Exercise the features that depend on mlflow.
- Repeat the inspection and confirm that only fixed versions remain installed.

## Rollback considerations
- Keep the previous lockfile and deployment artifact. Rolling back reinstates the vulnerable version, so apply any documented mitigation while you fix forward.

## Unresolved questions
- Which deployments run an affected version?
- Exploitation status can change. Check again before lowering the priority.
- Mark the work resolved only after the fixed version is verified in production.

## Sources
- Verified: https://github.com/advisories/GHSA-r23q-823p-vmf7 (GitHub-reviewed advisory GHSA-r23q-823p-vmf7 lists affected ranges: PyPI mlflow >=0 <3.8.1)
- Verified: https://osv.dev/vulnerability/GHSA-r23q-823p-vmf7 (OSV record GHSA-r23q-823p-vmf7 lists affected ranges: PyPI mlflow >=0 <3.8.1 (fixed))

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