信息网络安全 ›› 2025, Vol. 25 ›› Issue (9): 1385-1396.doi: 10.3969/j.issn.1671-1122.2025.09.007

• 入选论文 • 上一篇    下一篇

基于污点分析与文本语义的固件程序交互关系智能逆向分析方法

王磊, 陈炯峄, 王剑(), 冯袁   

  1. 国防科技大学电子科学学院,长沙 410003
  • 收稿日期:2025-05-29 出版日期:2025-09-10 发布日期:2025-09-18
  • 通讯作者: 王剑 jwang@nudt.edu.cn
  • 作者简介:王磊(1996—),男,河南,硕士研究生,主要研究方向为二进制程序逆向工程|陈炯峄(1993—),男,湖南,讲师,博士,CCF会员,主要研究方向为网络与系统安全|王剑(1975—),男,湖南,教授,博士,主要研究方向为网络空间对抗、漏洞分析与检测、无线网络安全|冯袁(2002—),男,湖北,博士研究生,主要研究方向为二进制程序逆向工程
  • 基金资助:
    国家自然科学基金(62302508)

Intelligent Reverse Analysis Method of Firmware Program Interaction Relationships Based on Taint Analysis and Textual Semantics

WANG Lei, CHEN Jiongyi, WANG Jian(), FENG Yuan   

  1. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410003, China
  • Received:2025-05-29 Online:2025-09-10 Published:2025-09-18

摘要:

针对嵌入式设备固件程序间交互关系逆向分析自动化程度低、准确率不高、分析效率低等问题,文章提出一种基于污点分析与文本语义的固件程序交互关系智能逆向分析方法。该方法构建了基于污点分析的关联函数代码切片算法,结合大语言模型的语义理解能力,实现了二进制程序中交互信息的精准提取和关联代码片段的智能定位,此外,还设计了面向脚本文件和配置文件的专用交互信息提取方法,有效提升了方法处理非结构化文本数据的能力。实验结果表明,程序间交互关系逆向分析方法的检测准确率达93.2%,研究成果可为理解程序功能、实现通信控制、发现潜在漏洞等应用提供有效支撑。

关键词: 污点分析, 大语言模型, 逆向分析, 程序交互

Abstract:

To address the challenges of low automation, limited accuracy and inefficiency in reverse-engineering interaction relationships among embedded firmware programs, this paper proposed an intelligent reverse analysis method based on taint analysis and textual semantics. The approach introduced a taint-analysis-based associated function code slicing algorithm, which combined with the semantic comprehension capabilities of large language models, enabled precise extraction of interaction-related information from binary programs and intelligent localization of relevant code segments. Furthermore, a dedicated interaction extraction method was designed for script and configuration files, significantly enhancing the ability of method to process unstructured textual data. The experimental results demonstrate that the proposed method achieves an interaction detection accuracy of 93.2%. The findings provide robust support for program functionality comprehension, communication control, and vulnerability discovery in practical applications.

Key words: taint analysis, large language models, reverse analysis, program interaction

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