Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks

A team of researchers from Booz Allen Hamilton and the University of Maryland has published a paper analyzing the use of Chain-of-Thought (CoT) prompting for the complex task of code deobfuscation. The study focused on reversing control flow obfuscation techniques—specifically Control Flow Flattening (CFF) and Opaque Predicates—which are commonly used to protect software and hide malware. The researchers evaluated five state-of-the-art large language models (LLMs) to see if guiding them through explicit, step-by-step reasoning could improve their ability to recover readable, functional code from obfuscated versions.

Their results were significant. Across a diverse set of standard C benchmarks, CoT prompting substantially outperformed simple zero-shot prompting. Among the models tested, GPT-5 achieved the strongest overall performance. When using CoT, GPT-5 showed an average gain of about 16% in accurately reconstructing the original control-flow graph and a 20.5% improvement in preserving the program's semantics (its actual behavior). The study also found that performance depends on the original code's complexity and the specific obfuscator used.

Collectively, the findings suggest that CoT-guided LLMs, particularly advanced models like GPT-5, can serve as powerful assistants for reverse engineers and security analysts. This approach provides improved code explainability and more faithful reconstruction while potentially slashing the manual effort required for deobfuscation from days or months down to a fraction of the time. It represents a practical fusion of AI reasoning techniques with critical software security workflows.