MLflow Statsmodels Flavor Security Control Bypass

MLflow contains a security vulnerability where the mlflow.statsmodels flavor bypasses the MLFLOW_ALLOW_PICKLE_DESERIALIZATION security control. This control was originally introduced to prevent unsafe pickle.load execution during model loading, specifically to mitigate risks associated with CVE-2024-37052 through CVE-2024-37060. When operators set this variable to False, they intend to block all pickle-based deserialization. However, the mlflow.statsmodels implementation completely omits this security guard. An attacker who can upload or place a crafted MLmodel artifact into an accessible artifact store can trigger arbitrary code execution on any system or process that invokes mlflow.pyfunc.load_model() against the malicious model, regardless of the environment configuration. This vulnerability effectively nullifies a primary defense-in-depth measure against remote code execution in MLflow deployments.

Attack Chain

1. Attacker identifies an accessible MLflow artifact store or model registry without authentication (default deployments).
2. Attacker crafts a malicious model.pkl payload that triggers command execution upon deserialization.
3. Attacker creates a corresponding MLmodel YAML file specifying mlflow.statsmodels as the loader module.
4. Attacker uploads the malicious model.pkl and MLmodel files to the target artifact store.
5. Attacker influences a target application or ML pipeline to call mlflow.pyfunc.load_model() with the path to the malicious model.
6. The mlflow.pyfunc.load_model() function dispatches the load request to mlflow.statsmodels._load_pyfunc().
7. mlflow.statsmodels executes smio.load_pickle() without checking the MLFLOW_ALLOW_PICKLE_DESERIALIZATION environment variable.
8. The malicious pickle payload deserializes, resulting in arbitrary code execution with the privileges of the calling process.

Impact

Successful exploitation allows unauthenticated attackers to execute arbitrary code with the permissions of the MLflow service or the application loading the model. This can lead to full system compromise, exfiltration of sensitive model data, or persistence within the environment. Targeted sectors include organizations leveraging MLflow for MLOps, particularly those with internet-exposed model registries lacking authentication.

Recommendation

1. Upgrade MLflow to version 3.15.0 or later immediately to include the guard logic.
2. Implement strict authentication for all MLflow artifact stores and model registries to prevent unauthorized model uploads.
3. Audit all artifact stores for suspicious or unknown MLmodel files referencing the mlflow.statsmodels flavor.
4. If upgrading is not immediately feasible, implement strict file-system access controls (ACLs) on model storage locations to ensure only trusted service identities can modify model artifacts.