LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution

📰 ArXiv cs.AI

Learn how to leverage code-centric large language models for malware attribution and improve static malware analysis with the LCC-LLM framework

advanced Published 9 May 2026

Action Steps

Build a code-centric benchmark dataset using LCC-LLM to improve malware analysis
Configure the LCC-LLM framework to integrate with existing malware detection tools
Apply the LCC-LLM model to identify malicious and vulnerable code segments in malware samples
Test the effectiveness of LCC-LLM in attributing malware to specific threat actors
Compare the performance of LCC-LLM with traditional malware attribution methods

Who Needs to Know This

Security researchers and malware analysts can benefit from this framework to enhance their malware attribution capabilities and identify vulnerable code segments more effectively

Key Insight

💡 Code-centric large language models can enhance malware attribution by providing evidence-grounded insights into malicious code segments

Key Takeaways

Learn how to leverage code-centric large language models for malware attribution and improve static malware analysis with the LCC-LLM framework

Full Article

Title: LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution

Abstract:
arXiv:2605.05807v1 Announce Type: cross Abstract: LLMs are increasingly explored for malware analysis; however, current LLM-based malware attribution remains limited by unsupported indicators and insufficient code-level grounding for identifying malicious and vulnerable code segments. To address these limitations, this research introduces LCC-LLM, a code-centric benchmark dataset and evidence-grounded framework for malware attribution and multi-task static malware analysis. The proposed LCCD dat

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LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution

Key Takeaways

Full Article

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