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LLMs improve malware classification with multi-decompiler analysis · 1 source tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method using Large Language Models (LLMs) to improve malware classification by analyzing decompiled code from multiple sources. The study found that using decompiled outputs from both Ghidra and RetDec, rather than just one, significantly enhances the accuracy of identifying malicious software. This multi-view approach increases recall for malicious samples and provides complementary evidence, as the two decompilers make different types of errors. AI

IMPACT Enhances LLM capabilities in cybersecurity, potentially improving malware detection efficiency.

RANK_REASON Academic paper on a novel application of LLMs to a security problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs improve malware classification with multi-decompiler analysis · 1 source tracked

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Bercan Turkmen, Vyas Raina ·

    Multi-View Decompilation for LLM-Based Malware Classification

    arXiv:2606.20436v1 Announce Type: cross Abstract: Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable. Recent work suggests that large language models (LLMs) can assist this process by classifying decompiled code as benign…

  2. arXiv cs.AI TIER_1 English(EN) · Vyas Raina ·

    Multi-View Decompilation for LLM-Based Malware Classification

    Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable. Recent work suggests that large language models (LLMs) can assist this process by classifying decompiled code as benign or malicious, but existing pipelines typically re…

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