Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is caused due to inadequate input validation in the _load_model function within mlflow/pytorch/__init__.py. This allows an attacker to 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.