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GPT-5 shows improved code deobfuscation with Chain-of-Thought prompting

By PulseAugur Editorial · [1 sources] ·

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

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.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GPT-5 shows improved code deobfuscation with Chain-of-Thought prompting

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Seyedreza Mohseni, Sarvesh Baskar, Edward Raff, Manas Gaur ·

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

    arXiv:2604.15390v3 Announce Type: replace-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 t…

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