Multi-View Decompilation for LLM-Based Malware Classification

Multi-View Decompilation for LLM-Based Malware Classification arXiv:2606.20436v1 Announce Type: new 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 or malicious, but existing pipelines typically rely on a single decompiler view. We argue that this assumption is fragile: decompilers are…

https://arxiv.org/abs/2606.20436

sha256:48c23d9878d4a086ef73dedcb823fd7962548ffeac434bc6ce61b767313ddc02

• cve0
• md50
• url0
• ipv40
• ipv60
• sha10
• domain0
• sha2560
• malware_token0
• attack_pattern0
• threat_actor_token0