Assessing LLMs in Malicious Code Deobfuscation of Real-world Malware Campaigns

arXiv:2404.19715v1 Announce Type: new
Abstract: The integration of large language models (LLMs) into various pipelines is increasingly widespread, effectively automating many manual tasks and often surpassing human capabilities. Cybersecurity researchers and practitioners have recognised this potential. Thus, they are actively exploring its applications, given the vast volume of heterogeneous data that requires processing to identify anomalies, potential bypasses, attacks, and fraudulent incidents. On top of this, LLMs' advanced capabilities in generating functional code, comprehending code context, and summarising its operations …

applications arxiv campaigns capabilities code cs.cr cybersecurity data deobfuscation effectively human integration language language models large llms malicious malware pipelines real researchers vast world