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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%). 

Decompilation is a crucial capability in forensic analysis, facilitating analysis of unknown binaries. The recent rise of Python malware has brought attention to Python decompilers that aim to obtain source code representation from a Python binary. However, Python decompilers fail to handle various binaries, limiting their capabilities in forensic analysis. This paper proposes a novel solution that transforms a decompilation error-inducing Python binary into a decompilable binary. Our key intuition is that we can resolve the decompilation errors by transforming error-inducing code blocks in the input binary into another form. The core of our approach is the concept of Forensically Equivalent Transformation (FET) which allows non-semantic preserving transformation in the context of forensic analysis. We carefully define the FETs to minimize their undesirable consequences while fixing various error-inducing instructions that are difficult to solve when preserving the exact semantics. We evaluate the prototype of our approach with 17,117 real-world Python malware samples causing decompilation errors in five popular decompilers. It successfully identifies and fixes 77,022 errors. Our approach also handles anti-analysis techniques, including opcode remap- ping, and helps migrate Python 3.9 binaries to 3.8 binaries.