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