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 remapping, and helps migrate Python 3.9 binaries to 3.8 binaries.

Fuzzing is a practical technique to automatically find vulnerabilities in software. It is well-suited to running at scale with distributed computing platforms thanks to its parallelizability. Therefore, individual researchers and companies typically setup fuzzing platforms on multiple servers and run fuzzers in parallel. However, as such resources are private, they suffer from financial and physical limits. In this paper, we propose [email protected]; the first public collaborative fuzzing network, based on heterogeneous machines owned by potentially untrusted users. Using our system, multiple organizations (or individuals) can easily collaborate to fuzz a software of common interest in an efficient way. One can participate and earn economic benefits if the fuzzing network is tied to a bug-bounty program, or simply donate spare computing power as a volunteer.

Rust is a promising system programming language that guarantees memory safety at compile time. To support diverse requirements for system software such as accessing low-level hardware, Rust allows programmers to perform operations that are not protected by the Rust compiler with the unsafe keyword. However, Rust's safety guarantee relies on the soundness of all unsafe code in the program as well as the standard and external libraries, making it hard to reason about their correctness. In other words, a single bug in any unsafe code breaks the whole program's safety guarantee.