Paper Detail
Hamed Jelodar, Samita Bai, Tochukwu Emmanuel Nwankwo, Parisa Hamedi, Mohammad Meymani, Roozbeh Razavi-Far, Ali A. Ghorbani
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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@article{jelodar2026llm4codere,
title = {LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering},
author = {Hamed Jelodar and Samita Bai and Tochukwu Emmanuel Nwankwo and Parisa Hamedi and Mohammad Meymani and Roozbeh Razavi-Far and Ali A. Ghorbani},
year = {2026},
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 en},
url = {https://arxiv.org/abs/2604.06095},
keywords = {cs.CR, cs.AI},
eprint = {2604.06095},
archiveprefix = {arXiv},
}
{}