MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization

arXiv:2406.18379v1 Announce Type: new
Abstract: Binary malware summarization aims to automatically generate human-readable descriptions of malware behaviors from executable files, facilitating tasks like malware cracking and detection. Previous methods based on Large Language Models (LLMs) have shown great promise. However, they still face significant issues, including poor usability, inaccurate explanations, and incomplete summaries, primarily due to the obscure pseudocode structure and the lack of malware training summaries. Further, calling relationships between functions, which involve the rich interactions within a binary …

arxiv behaviors binary code cracking cs.ai cs.cr cs.se descriptions detection files great human issues language language models large llms malicious malware poor pseudocode source code