LLMs leverage code analysis for improved malware attribution

Researchers have developed LCC-LLM, a framework and dataset designed to improve malware attribution using large language models. The system leverages code-centric representations, including decompiled C code and assembly, to provide deeper analysis than previous methods. LCC-LLM integrates a retrieval-augmented generation pipeline with cybersecurity knowledge to enhance factual reliability and analyst decision support, showing promising results in structured report generation and malware classification. AI

影响 Enhances LLM capabilities for cybersecurity, potentially improving threat intelligence and incident response.

排序理由 This is a research paper detailing a new framework and dataset for malware analysis. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• arXiv
• DeepSeek-R1-Distill-Qwen-14B
• LangGraph
• LCC-LLM
• MalwareBazaar
• MITRE ATT&CK
• Qwen3-Coder-30B-A3B

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