- CVSS
- CRITICAL · 9.8v3.1CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
- Published
- 2026-03-30
- Weakness
- CWE-77, CWE-78
- Source
- nvd.nist.gov/vuln/detail/CVE-2025-15379
Description
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.
References
- https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05e
- https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75
- https://access.redhat.com/security/cve/CVE-2025-15379
- https://bugzilla.redhat.com/show_bug.cgi?id=2452949
- https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.json
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2025-15379 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.