Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks
Summary: arXiv:2604.15390v1 Announce Type: cross
Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires days or even months of manual work with complex and expensive analysis tools. In this paper, we explore an alternative approach based on Chain-of-Thought (CoT) prompting, where a large language model is guided through explicit, step-by-step reasoning tailored for code analysis.
Introduction
Control flow obfuscation techniques, such as Control Flow Flattening (CFF) and Opaque Predicates, are commonly employed to make reverse engineering of software more challenging. These methods complicate the understanding of a program’s structure and behavior, leading to a growing need for effective deobfuscation strategies that can automate this intricate process.
Chain-of-Thought (CoT) Prompting
CoT prompting is a novel technique that enhances the performance of large language models by breaking down complex tasks into manageable, sequential steps. This methodology not only aids in better comprehension of the code but also improves the accuracy of the deobfuscation process. By implementing CoT prompting, researchers have identified significant improvements in the structural recovery of control flow graphs and the preservation of program semantics.
Methodology
Our study evaluates five state-of-the-art large language models, with a focus on their performance in deobfuscation tasks using CoT prompting. The evaluation is based on:
- Structural Recovery: Measuring the accuracy of reconstructed control flow graphs.
- Semantic Preservation: Assessing the similarity of program behavior before and after deobfuscation.
We applied our methodology to a diverse set of standard C benchmarks to ensure a comprehensive evaluation.
Results
The results indicate that CoT prompting significantly enhances deobfuscation quality compared to traditional zero-shot prompting methods. Notably, GPT5 emerged as the top-performing model, achieving:
- An average gain of about 16% in control-flow graph reconstruction.
- A 20.5% improvement in semantic preservation across the evaluated benchmarks.
Additionally, the performance of the models was influenced by various factors, including the level of obfuscation applied and the intrinsic complexity of the original control flow graph.
Conclusion
The findings of this study underscore the potential of CoT-guided large language models as effective tools for code deobfuscation. They not only facilitate improved code explainability but also contribute to more accurate control flow graph reconstructions and better preservation of program behavior. This advancement may ultimately lead to a reduction in the manual effort required for reverse engineering, making the deobfuscation process more efficient and accessible.