LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering

• URL: http://arxiv.org/abs/2604.06095v1
• Date: Tue, 07 Apr 2026 17:08:44 GMT
• Title: LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering
• Abstract summary: LLM4CodeRE is a domain-adaptive LLM framework for bidirectional code reverse engineering.<n>It supports both assembly-to-source decompilation and source-to-assembly translation within a unified model.<n> Experimental results demonstrate that LLM4CodeRE outperforms existing decompilation tools and general-purpose code models.
• Score: 4.240884806677703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code decompilation analysis is a fundamental yet challenging task in malware reverse engineering, particularly due to the pervasive use of sophisticated obfuscation techniques. Although recent large language models (LLMs) have shown promise in translating low-level representations into high-level source code, most existing approaches rely on generic code pretraining and lack adaptation to malicious software. We propose LLM4CodeRE, a domain-adaptive LLM framework for bidirectional code reverse engineering that supports both assembly-to-source decompilation and source-to-assembly translation within a unified model. To enable effective task adaptation, we introduce two complementary fine-tuning strategies: (i) a Multi-Adapter approach for task-specific syntactic and semantic alignment, and (ii) a Seq2Seq Unified approach using task-conditioned prefixes to enforce end-to-end generation constraints. Experimental results demonstrate that LLM4CodeRE outperforms existing decompilation tools and general-purpose code models, achieving robust bidirectional generalization.

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