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Data processing oriented software, especially machine learning applications, are heavily dependent on standard frameworks/libraries such as TensorFlow and OpenCV. As those frameworks have gained significant popularity, the exploitation of vulnerabilities in the frameworks has become a critical security concern. While software isolation can minimize the impact of exploitation, existing approaches suffer from difficulty analyzing complex program dependencies or excessive overhead, making them ineffective in practice. We propose FreePart, a framework-focused software partitioning technique specialized for data processing applications. It is based on an observation that the execution of a data processing application, including data flows and usage of critical data, is closely related to the invocations of framework APIs. Hence, we conduct a temporal partitioning of the host application’s execution based on the invocations of framework APIs and the data objects used by the APIs. By focusing on data accesses at runtime instead of static program code, it provides effective and practical isolation from the perspective of data. Our evaluation on 23 applications using popular frameworks (e.g., OpenCV, Caffe, PyTorch, and TensorFlow) shows that FreePart is effective against all attacks composed of 18 real-world vulnerabilities with a low overhead (3.68%). 

https://doi.ieeecomputersociety.org/10.1109/SP46215.2023.00128