A Decompilation-Driven Framework for Malware Detection with Large Language Models

The parallel evolution of Large Language Models (LLMs) with advanced code-understanding capabilities and the increasing sophistication of malware presents a new frontier for cybersecurity research. This paper evaluates the efficacy of state-of-the-art LLMs in classifying executable code as either benign or malicious. We introduce an automated pipeline that first decompiles Windows executable into a C code using Ghidra disassembler and then leverages LLMs to perform the classification. Our evaluation reveals that while standard LLMs show promise, they are not yet robust enough to replace traditional anti-virus software. We demonstrate that a fine-tuned model, trained on curated malware and benign datasets, significantly outperforms its vanilla counterpart. However, the performance of even this specialized model degrades notably when encountering newer malware. This finding demonstrates the critical need for continuous fine-tuning with emerging threats to maintain model effectiveness against the changing coding patterns and behaviors of malicious software.

Proactively Detecting Threats: A Novel Approach Using LLMs

Enterprise security faces escalating threats from sophisticated malware, compounded by expanding digital operations. This paper presents the first systematic evaluation of large language models (LLMs) to proactively identify indicators of compromise (IOCs) from unstructured web-based threat intelligence sources, distinguishing it from reactive malware detection approaches. We developed an automated system that pulls IOCs from 15 web-based threat report sources to evaluate six LLM models (Gemini, Qwen, and Llama variants). Our evaluation of 479 webpages containing 2,658 IOCs (711 IPv4 addresses, 502 IPv6 addresses, 1,445 domains) reveals significant performance variations. Gemini 1.5 Pro achieved 0.958 precision and 0.788 specificity for malicious IOC identification, while demonstrating perfect recall (1.0) for actual threats.