WS-2026-0057
Published:September 01, 2026
Updated:September 02, 2026
MLflow's mlflow.statsmodels flavor loaded pickled model artifacts via smio.load_pickle() in _load_model() without consulting the MLFLOW_ALLOW_PICKLE_DESERIALIZATION safety control, so setting that variable to false did not prevent deserialization. A crafted model artifact therefore achieves remote code execution on load even when the operator had explicitly disabled pickle deserialization. Fixed in 3.15.0 by adding the guard to the statsmodels flavor.
Affected Packages
mlflow (CONDA):
Affected version(s) >=2.1.1 <3.15.0Fix Suggestion:
Update to version 3.15.0https://github.com/mlflow/mlflow.git (GITHUB):
Affected version(s) >=v2.1.0 <v3.15.0Fix Suggestion:
Update to version v3.15.0mlflow (PYTHON):
Affected version(s) >=2.1.0 <3.15.0Fix Suggestion:
Update to version 3.15.0Related Resources (3)
Do you need more information?
Contact UsCVSS v4
Base Score:
8.7
Attack Vector
NETWORK
Attack Complexity
LOW
Attack Requirements
NONE
Privileges Required
NONE
User Interaction
PASSIVE
Vulnerable System Confidentiality
HIGH
Vulnerable System Integrity
HIGH
Vulnerable System Availability
HIGH
Subsequent System Confidentiality
NONE
Subsequent System Integrity
NONE
Subsequent System Availability
NONE
CVSS v3
Base Score:
8.8
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
NONE
User Interaction
REQUIRED
Scope
UNCHANGED
Confidentiality
HIGH
Integrity
HIGH
Availability
HIGH
Weakness Type (CWE)
Deserialization of Untrusted Data