Deserialization of Untrusted Data Affecting mlflow package, versions [1.1.0,]

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_model_from_local_file function in the sklearn/__init__.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model, which will then be deserialized when the model is loaded.

with mlflow.start_run():
     lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
     lr.fit(train_x, train_y)

     ...

     # Either upload model which will use default format of cloudpickle
mlflow.sklearn.log_model(lr, artifact_path="model", registered_model_name="SklearnPickleDefault")

# Or upload model and set serialiazation format to pickle
mlflow.sklearn.log_model(lr, artifact_path="model", registered_model_name="SklearnPickleDefault", serialization_format='pickle'
)

Serialization is a process of converting an object into a sequence of bytes which can be persisted to a disk or database or can be sent through streams. The reverse process of creating object from sequence of bytes is called deserialization. Serialization is commonly used for communication (sharing objects between multiple hosts) and persistence (store the object state in a file or a database). It is an integral part of popular protocols like Remote Method Invocation (RMI), Java Management Extension (JMX), Java Messaging System (JMS), Action Message Format (AMF), Java Server Faces (JSF) ViewState, etc.

Deserialization of untrusted data (CWE-502) is when the application deserializes untrusted data without sufficiently verifying that the resulting data will be valid, thus allowing the attacker to control the state or the flow of the execution.

• Vulnerability Report
• Vulnerable Code