CVE-2025-15379 is a critical-severity (CVSS 9) CWE-77 vulnerability in mlflow. A fix is available for mlflow — see the affected versions and patch details below.
Command Injection in mlflow/mlflow
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for CVE-2025-15379.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
Where this sits among everything scored
Real-World Exposure
mlflowReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
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 Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | mlflow | all versions | 3.8.1pip install --upgrade 'mlflow==3.8.1' |
Affected Products
mlflowlfprojectsDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for mlflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update mlflow to 3.8.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2025-15379 is resolved across your whole dependency graph.
Workarounds
Stop passing untrusted input into the interpreter or shell: call the affected binary with an argument array rather than a composed command string, reject anything outside a strict allowlist of expected values, and run the component under an account that cannot reach beyond the work it legitimately does.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
This vulnerability in MLflow allows for arbitrary command execution when deploying a model with `env_manager=LOCAL`. An attacker can supply a malicious model artifact containing a crafted `python_env.yaml` file, leading to command injection during container initialization. During the deployment of the model MLflow…
To mitigate this issue, avoid deploying MLflow models with `env_manager=LOCAL`. If using `env_manager=LOCAL` is unavoidable, ensure that all model artifacts, particularly their `python_env.yaml` files, originate from trusted sources and are thoroughly vetted for malicious content. This operational control helps prevent the injection of arbitrary commands during model serving container initialization.Source: Red Hat security advisory for CVE-2025-15379 (CC BY 4.0)
Frequently Asked Questions
Is CVE-2025-15379 in your dependencies?
Find it across PyPI, including transitive dependencies.