Shaoyin Cheng

Gangyang Li, Xiuwei Shang, Shaoyin Cheng et al.

Type recovery is a crucial step in binary code analysis, holding significant importance for reverse engineering and various security applications. Existing works typically simply target type identifiers within binary code and achieve type recovery by analyzing variable characteristics within functions. However, we find that the types in real-world binary programs are more complex and often follow specific distribution patterns. In this paper, to gain a profound understanding of the variable type recovery problem in binary code, we first conduct a comprehensive empirical study. We utilize the TYDA dataset, which includes 163,643 binary programs across four architectures and four compiler optimization options, fully reflecting the complexity and diversity of real-world programs. We carefully study the unique patterns that characterize types and variables in binary code, and also investigate the impact of compiler optimizations on them, yielding many valuable insights. Based on our empirical findings, we propose ByteTR, a framework for recovering variable types in binary code. We decouple the target type set to address the issue of unbalanced type distribution and perform static program analysis to tackle the impact of compiler optimizations on variable storage. In light of the ubiquity of variable propagation across functions observed in our study, ByteTR conducts inter-procedural analysis to trace variable propagation and employs a gated graph neural network to capture long-range data flow dependencies for variable type recovery. We conduct extensive experiments to evaluate the performance of ByteTR. The results demonstrate that ByteTR leads state-of-the-art works in both effectiveness and efficiency. Moreover, in real CTF challenge case, the pseudo code optimized by ByteTR significantly improves readability, surpassing leading tools IDA and Ghidra.

Li Hu, Xiuwei Shang, Jieke Shi et al.

Deobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large language models (LLMs) have shown promise in recovering semantics from obfuscated binaries, a systematic evaluation of their effectiveness is still lacking. In this work, we present BinDeObfBench, the first comprehensive benchmark for assessing LLM-based binary deobfuscation across diverse transformations spanning pre-compilation, compile-time, and post-compilation stages. Our evaluation shows that deobfuscation performance depends more on reasoning capability and domain expertise than on model scale, and that task-specific supervised fine-tuning consistently outperforms broad domain pre-training. Reasoning models can maintain robustness under severe obfuscation, generalize across different instruction set architectures (ISAs) and optimization levels. In-context learning benefits standard models but yields limited gains for reasoning models. Overall, our study highlights the importance of task-specific fine-tuning and reasoning-driven strategies, and positions BinDeObfBench as a basis for future work in binary deobfuscation.

Guoqiang Chen, Lingyun Ying, Ziyang Song et al.

Retrieving binary code via natural language queries is a pivotal capability for downstream tasks in the software security domain, such as vulnerability detection and malware analysis. However, it is challenging to identify binary functions semantically relevant to the user query from thousands of candidates, as the absence of symbolic information distinguishes this task from source code retrieval. In this paper, we introduce, BinSeek, a two-stage cross-modal retrieval framework for stripped binary code analysis. It consists of two models: BinSeek-Embedding is trained on large-scale dataset to learn the semantic relevance of the binary code and the natural language description, furthermore, BinSeek-Reranker learns to carefully judge the relevance of the candidate code to the description with context augmentation. To this end, we built an LLM-based data synthesis pipeline to automate training construction, also deriving a domain benchmark for future research. Our evaluation results show that BinSeek achieved the state-of-the-art performance, surpassing the the same scale models by 31.42% in Rec@3 and 27.17% in MRR@3, as well as leading the advanced general-purpose models that have 16 times larger parameters.