# CVE-2025-15379: MLflow command injection in model serving container initialization

Source: https://junglewise.ai/threats/cve-2025-15379-mlflow-command-injection-in-model-serving-container  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-03-30. Updated: 2026-06-30  
License: CC BY 4.0 with a link back

CVE-2025-15379 · Severity: critical · CVSS 9.8 · Published 2026-03-30

MLflow is an open-source platform used to manage the machine learning lifecycle, including model deployment. A security flaw allows an attacker to execute malicious commands on the server by providing a specially crafted model file. This could lead to a complete takeover of the system hosting the machine learning models, potentially exposing sensitive data or disrupting operations.

Technologies: [mlflow (PyPI)](https://junglewise.ai/threats/technologies/mlflow). Vendors: [PyPI](https://junglewise.ai/threats/vendors/pypi).

## Quick answers

**Is CVE-2025-15379 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2025-15379 being exploited in the wild.

**How severe is CVE-2025-15379?** Critical severity, with a CVSS score of 9.8, published 30 March 2026.

**Which products and versions are affected?** mlflow (PyPI) ECOSYSTEM:introduced=0,fixed=3.8.1.

**Is there a fix for CVE-2025-15379?** Yes. mlflow (PyPI): upgrade to 3.8.1 or later. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

**How is CVE-2025-15379 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2025-15379?** Check whether you run mlflow (PyPI) at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

## How to fix

- mlflow (PyPI): upgrade to 3.8.1 or later

  ```sh
  pip install "mlflow>=3.8.1"
  ```


## Technical details

A command injection vulnerability exists in MLflow's model serving container initialization code within the `_install_model_dependencies_to_env()` function. When a model is deployed using the `env_manager=LOCAL` configuration, the application reads dependency specifications from the model artifact's `python_env.yaml` file. These specifications are directly interpolated into a shell command without proper sanitization or escaping. An attacker can exploit this by supplying a malicious model artifact containing shell metacharacters in the dependency fields, leading to arbitrary command execution with the privileges of the MLflow process. The issue is fixed in version 3.8.2 by implementing proper shell escaping using `shlex`.

## Affected products

- LF Projects MLflow 3.8.0 to 3.8.1
- Red Hat OpenShift AI (RHOAI)

## Identifiers

- CVE-2025-15379
- GHSA-r23q-823p-vmf7
- PYSEC-2026-423

## Timeline

- 2025-12-24: patched: Fix committed to GitHub repository
- 2026-03-30: disclosed: Initial disclosure by huntr.dev and NVD publication

## References

- [https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e](https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e)
- [https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75](https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75)
- [https://access.redhat.com/security/cve/CVE-2025-15379](https://access.redhat.com/security/cve/CVE-2025-15379)
- [https://bugzilla.redhat.com/show_bug.cgi?id=2452949](https://bugzilla.redhat.com/show_bug.cgi?id=2452949)
- [https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json](https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json)

## Related threats

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- [MLflow credential leak in CrewAI autolog](https://junglewise.ai/threats/cve-2026-116691-mlflow-is-an-open-source-ai-engineering-platform-for-agents)
- [MLflow path traversal in prompt model version artifact access](https://junglewise.ai/threats/cve-2026-116690-mlflow-is-an-open-source-ai-engineering-platform-for-agents)
- [MLflow unsafe deserialization in scorer endpoints](https://junglewise.ai/threats/cve-2026-115886-mlflow-is-an-open-source-ai-engineering-platform-for-agents)
- [MLflow FileStore path traversal in trace storage](https://junglewise.ai/threats/cve-2026-53995-mlflow-3-17-1-and-earlier-contains-a-path-traversal-vulnerability)