CVE-2024-37053

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.

Weakness

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

Affected Software

Potential Mitigations

• Make fields transient to protect them from deserialization.
• An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.

Related Attack Patterns

• https://cwe.mitre.org/data/definitions/586.html

References

• https://hiddenlayer.com/sai-security-advisory/mlflow-june2024
• https://hiddenlayer.com/sai-security-advisory/mlflow-june2024