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

TL;DR

This paper surveys the landscape of using Large Language Models for malware defense, proposing a structured taxonomy, problem formulation with quantitative metrics, and a risk-mitigation framework. It identifies five key LLM-enabled defense applications and provides explicit metrics to enable rigorous evaluation. The authors also present guiding principles and concrete mitigation strategies, validated conceptually with synthetic data. They acknowledge limitations such as false positives and explainability challenges, and highlight avenues for future work in advanced devices and emerging threat modalities.

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.

Figures (6)

• Figure 2: Visual taxonomy of the overlapping categories of classifications within the literature base. Each colored circle represents a distinct category, and the overlapping regions indicate the shared literature pieces between these categories.
• Figure 3: Hierarchical classification of various methods through which LLMs can be employed to detect malware. The central node represents the core LLM technology, while the primary branches emanating from it illustrate specific techniques and applications. The secondary branches provide discussion points of their respective parent nodes.
• Figure 4: Graphical depiction of the mechanics of the proposed metric framework. The diagrams capture the five ways in which LLMs can be used for malware detection, involving simple counting in the case of malware honeypots and sophisticated optimization routines in the case of using existing malware code data to train for the detection of new threats.
• Figure 5: A graphical representation of the key guiding principles for effectively utilizing LLMs in malware detection. The diagram outlines a systematic approach, emphasizing the importance of data quality, model training, human oversight, and integration with existing security systems.
• Figure 6: A graphical rendition of the proposed risk mitigation strategy for utilizing LLMs in malware detection. The diagram outlines a multi-faceted approach that encompasses data diversity, continuous learning, real-time code scanning, targeted sandboxing, and federated training.