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

arXiv: https://arxiv.org/abs/2409.07587 · HTML: https://mlsanity.com/paper/2409.07587/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2024.

## Built on by (most cited first, 13 of 13)

- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [A Framework for Evaluating Emerging Cyberattack Capabilities of AI](https://mlsanity.com/paper/2503.11917/) (2025)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting](https://mlsanity.com/paper/2509.23571/) (2025)
- [REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinement](https://mlsanity.com/paper/2510.03588/) (2025)
- [Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection](https://mlsanity.com/paper/2507.22772/) (2025)
- [A Decompilation-Driven Framework for Malware Detection with Large Language Models](https://mlsanity.com/paper/2601.09035/) (2026)
- [Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis](https://mlsanity.com/paper/2607.20216/) (2026)
- [MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation](https://mlsanity.com/paper/2507.02057/) (2025)
- [LLM-Based Identification of Infostealer Infection Vectors from Screenshots: The Case of Aurora](https://mlsanity.com/paper/2507.23611/) (2025)
- [XGen-Q: An Explainable Domain-Adaptive LLM Framework with Retrieval-Augmented Generation for Software Security](https://mlsanity.com/paper/2510.19006/) (2025)
- [Accuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning vs. Full Fine-Tuning](https://mlsanity.com/paper/2511.19654/) (2025)
- [Multi-View Decompilation for LLM-Based Malware Classification](https://mlsanity.com/paper/2606.20436/) (2026)

## Abstract (arXiv)

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.