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.
Affected products
lfprojects: mlflow
References5
- github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef… Patch
- huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75 ExploitThird Party Advisory
- access.redhat.com/security/cve/CVE-2025-15379 Third Party Advisory
- bugzilla.redhat.com/show_bug.cgi?id=2452949 Third Party Advisory
- security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379… Third Party Advisory
Metrics
- CVSS 3.1
-
10.0
CRITICAL
Score source: NVD
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H - EPSS — exploit probability
- 2% percentile: 84 · as of 12 November 2026
- CWE
- CWE-77CWE-78
Could this be exploited in your environment?
A patch closes the known hole. A penetration test shows what an attacker can still reach — with proof.