Attributed metadata history
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intelligence_src_cd638cf167c7c119d6
observed · 2026-10-12 15:23:03 UTC
available
Fingerprint: e9e546feaa1a197f9c3a53bee19e29f6899cd295cb68e67b1de5b309ac3481e8
intelligence_src_cd638cf167c7c119d6
observed · 2026-10-12 15:23:03 UTC
Overview Two vulnerabilities in MLflow’s dspy and statsmodels model flavors allow unauthorized pickle deserialization executions despite a safety control. Specifically, the dspy flavor conditionally applies the control based on the model path’s file extension, and the statsmodels flavor does not apply the control. Description MLflow is an open-source platform for managing machine learning lifecycles, including model packaging, versioning, and deployment. "Flavors" refer to the specialized frameworks through which supported models are stored and loaded. In response to previous vulnerability concerns, MLflow implemented the MLFLOW_ALLOW_PICKLE_DESERIALIZATION safety control to block and disable executing any pickle deserialization and subsequent loads per the user’s choice. When loading models through mlflow.pyfunc.load_model(model), users must specify a model flavor and path in an MLmodel file. With the dspy flavor, MLflow checks the value of MLFLOW_ALLOW_PICKLE_DESERIALIZATION, and whether the specified model path ends in .pkl. A model path that does not end in .pkl (even if the file is actually a pickle file), will route to a separate branch for pickle deserialization, bypas
Fingerprint: 7210ab822fff701abd0790f3d9d1ebd2d36ccb3db1b8cb8de3bd56cedef1d941