UDA: Unified Pretraining for Multiarchitecture Binary Disassembly

The precision of binary disassembly is crucial for understanding program behavior in reverse engineering. However, existing disassembly tools struggle to accurately identify function boundaries when binary files are stripped, especially on reduced instruction set computer (RISC) architectures like ARM and MIPS. Additionally, disassembly tools often lack support for newer versions or less used architectures. In this paper, we propose UDA, a unified pre-training disassembly framework with multi-architecture support. Unified pre-training on both machine code and assembly code allows assembly code to help the semantic learning of machine code, thereby addressing the inherent semantic deficiencies of machine code. By extracting enhanced semantic features from machine code, UDA can not only more accurately identify function boundaries but also recover assembly code without relying on existing disassembly tools. UDA has been rigorously evaluated against both regular and obfuscated binary files. The results show that UDA improves the F1 score for function boundary detection by 0.9%, 7.9%, and 8.1% on x86, ARM, and MIPS architectures, respectively, compared to the state-of-the-art disassemblers. Additionally, it demonstrates strong robustness against unseen obfuscated binaries.