Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

TL;DR

This paper reviews the use of Large Language Models in malware detection, proposes a framework for assessing and mitigating risks, and demonstrates strategies to counter LLM-enabled malware threats.

Contribution

It introduces a comprehensive review, a classification scheme, performance metrics, and a risk mitigation framework specifically designed for LLM-related cybersecurity challenges.

Findings

Proposed effective risk mitigation strategies against LLM-enabled malware

Demonstrated the performance of mitigation strategies through evaluation

Established guiding principles for secure use of LLMs in cybersecurity

Abstract

The rising use of Large Language Models (LLMs) to create and disseminate malware poses a significant cybersecurity challenge due to their ability to generate and distribute attacks with ease. A single prompt can initiate a wide array of malicious activities. This paper addresses this critical issue through a multifaceted approach. First, we provide a comprehensive overview of LLMs and their role in malware detection from diverse sources. We examine five specific applications of LLMs: Malware honeypots, identification of text-based threats, code analysis for detecting malicious intent, trend analysis of malware, and detection of non-standard disguised malware. Our review includes a detailed analysis of the existing literature and establishes guiding principles for the secure use of LLMs. We also introduce a classification scheme to categorize the relevant literature. Second, we propose performance metrics to assess the effectiveness of LLMs in these contexts. Third, we present a risk mitigation framework designed to prevent malware by leveraging LLMs. Finally, we evaluate the performance of our proposed risk mitigation strategies against various factors and demonstrate their effectiveness in countering LLM-enabled malware. The paper concludes by suggesting future advancements and areas requiring deeper exploration in this fascinating field of artificial intelligence.