Deserialization of Untrusted Data Affecting mlflow package, versions [1.27.0,]

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 function in the BaseCard class within the recipes/cards/__init__.py file. An attacker can execute arbitrary code on the target system by creating an MLProject Recipe containing a malicious pickle file (e.g. pickle.pkl) and a python script that calls BaseCard.load(pickle.pkl). The pickle file will be deserialized when the project is run.

Note:

If you are not running MLflow on a publicly accessible server, this vulnerability won't apply to you.

name: RecipeTestingProject

conda_env: conda.yaml

entry_points:
 main:
      command: "python recipe_card_pickle.py"

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