Tootfinder

SK2Decompile: LLM-based Two-Phase Binary Decompilation from Skeleton to Skin
Hanzhuo Tan, Weihao Li, Xiaolong Tian, Siyi Wang, Jiaming Liu, Jing Li, Yuqun Zhang
https://arxiv.org/abs/2509.22114

SK2Decompile: LLM-based Two-Phase Binary Decompilation from Skeleton to Skin
Large Language Models (LLMs) have emerged as a promising approach for binary decompilation. However, the existing LLM-based decompilers still are somewhat limited in effectively presenting a program's source-level structure with its original identifiers. To mitigate this, we introduce SK2Decompile, a novel two-phase approach to decompile from the skeleton (semantic structure) to the skin (identifier) of programs. Specifically, we first apply a Structure Recovery model to translate a program's b…

SALT4Decompile: Inferring Source-level Abstract Logic Tree for LLM-Based Binary Decompilation
Yongpan Wang, Xin Xu, Xiaojie Zhu, Xiaodong Gu, Beijun Shen
https://arxiv.org/abs/2509.14646

SALT4Decompile: Inferring Source-level Abstract Logic Tree for LLM-Based Binary Decompilation
Decompilation is widely used in reverse engineering to recover high-level language code from binary executables. While recent approaches leveraging Large Language Models (LLMs) have shown promising progress, they typically treat assembly code as a linear sequence of instructions, overlooking arbitrary jump patterns and isolated data segments inherent to binary files. This limitation significantly hinders their ability to correctly infer source code semantics from assembly code. To address this …