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 or malicious, but existing pipelines typically rely on a single decompiler view. We argue that this assumption is fragile: decompilers are lossy heuristic tools, and different decompilers can expose different artefacts of the same binary. We curate a benchmark of benign utilities and malicious programs spanning a rang
The proliferation of LLMs and increasing sophistication of cyber threats are driving the need for advanced malware analysis techniques.
Improving LLM-based malware classification can significantly enhance cybersecurity defenses against increasingly complex and AI-assisted attacks.
The approach to malware analysis can shift from single-decompiler reliance to multi-view techniques, improving accuracy and resilience against obfuscation.
- · Cybersecurity companies
- · Security researchers
- · Organizations with critical infrastructure
- · Malware developers
- · Cyber adversaries
Enhanced ability to detect and classify unknown and polymorphic malware.
Increased costs and complexity for attackers to evade detection, potentially shifting attack vectors.
The development of adversarial AI techniques specifically designed to fool multi-view LLM classifiers, creating an arms race.
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Read at arXiv cs.AI