A new paper explores the use of Chain-of-Thought (CoT) prompting to improve large language models' ability to deobfuscate code, specifically focusing on control flow obfuscation techniques. The research evaluated five state-of-the-art models, finding that CoT prompting significantly enhances both structural recovery of control flow graphs and preservation of program semantics. GPT5 demonstrated the strongest performance, achieving substantial gains in reconstruction and semantic preservation compared to zero-shot prompting, suggesting CoT-guided LLMs can aid in reverse engineering tasks. AI
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IMPACT CoT-guided LLMs show promise in assisting with complex code deobfuscation, potentially reducing manual effort in reverse engineering.
RANK_REASON Academic paper analyzing LLM performance on a specific code analysis task.