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VulnerabilityPublished Updated

CVE-2025-15379: mlflow/mlflow · Command Injection

Affects mlflowSource GCVE Database

0
Authoritative record:CVE-2025-15379· Critical 10.0

Not known to be exploited · CVSS 10.0 Critical : upgrade to 3.8.1 as a priority.

Data as of 26 Oct 2026, 10:12 UTC

CVSS 3.0 base score
10.0Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
2.3% chance of exploitation in 30 days – higher than 83% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 3.8.1
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 30 Mar 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Upgrade mlflow to 3.8.1.

Affected software

ProductAffectedFixed in
Bitnamimlflowpkg:bitnami/mlflowOther threatsAffected
  • >=3.8.0 <3.8.1
Fixed in
  • 3.8.1

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

A fix is available in MLflow version 3.8.2. Users should upgrade to version 3.8.2 or later to remediate this vulnerability. If upgrading is not immediately possible, avoid deploying models with env_manager=LOCAL. If env_manager=LOCAL must be used, ensure that all model artifacts, especially python_env.yaml files, come from trusted sources and are thoroughly vetted to prevent malicious command injection.

Description

CVE-2025-15379 is a command injection vulnerability in MLflow's model serving container initialization code when deploying models with the env_manager set to LOCAL. The vulnerability arises because MLflow reads dependency specifications from the model artifact's python_env.yaml file and interpolates them directly into shell commands without sanitization. This allows an attacker who can supply a malicious model artifact to execute arbitrary commands with the permissions of the MLflow process. The flaw affects MLflow versions from 3.8.0 up to but not including 3.8.2, where it is fixed.

References

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