用大模型实现代码逆向工程双向翻译，更懂恶意软件
LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering
- 用双路径适配策略让模型理解恶意代码语法语义
- 在真实样本上比现有工具高18%的还原准确率
- 适合安全研究者和漏洞分析人员快速解析恶意程序
代码反编译分析是恶意软件逆向工程中的基础但极具挑战性的任务，尤其因广泛使用的复杂混淆技术而困难。尽管近期大语言模型（LLMs）在将低级表示转换为高级源码方面展现出潜力，但多数现有方法依赖通用代码预训练，缺乏对恶意软件的适应性。我们提出 LLM4CodeRE，一种面向双向代码逆向工程的领域自适应大模型框架，支持统一模型下的汇编到源码反编译与源码到汇编重构。为实现有效任务适配，引入两种互补微调策略：(i) 多适配器方法，用于特定任务的语法与语义对齐；(ii) 序列到序列统一方法，通过任务条件前缀强制端到端生成约束。实验结果表明，LLM4CodeRE优于现有反编译工具与通用代码模型，在多个数据集上实现稳健的双向泛化能力。
原文摘要 · Abstract (English)
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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