SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, it provides a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark.
Apache SystemDS suffer from an unauthenticated Java deserialization vulnerability in its federated worker. The federated worker (started with systemds -w) accepts objects over a Netty channel using ObjectDecoder with no deserialization filtering and no authentication. A remote attacker who can reach the worker port can send a serialized object that is deserialized with no class restriction. This results in unauthenticated remote code execution when a suitable gadget class is present on the federated worker's classpath.