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Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches
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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.
Forward citations
Cited by 7 Pith papers
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Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis
An evidence-grounded hybrid of 4B and 8B open-weight models reaches 35.30% on malware-report QA, slightly beating the best ungrounded frontier model (34.77%) while trailing a frontier model given the same evidence (38.22%).
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Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting
Standardized modular threat-hunting workflows (CyberTeam) improve LLM performance on blue team tasks compared to open-ended ICL, CoT, and ToT prompting across 30 tasks and 452k samples.
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MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation
MGC, a two-stage compiler framework, generates functional malware by decomposing malicious intents into benign-appearing MDIR components that strong aligned LLMs will implement, bypassing safety alignment.
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Efficient Code Analysis via Graph Representation Learning-Guided Large Language Models
A GNN selects suspicious subgraphs of a Python project and an LLM judges only those subgraphs, beating direct whole-project LLM analysis on PyPI malware benchmarks.
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Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Concept drift consistently lowers Android malware detection accuracy across nine machine learning and deep learning algorithms and two large language models, on two datasets.
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TraceRAG: A LLM-Based Framework for Explainable Android Malware Detection and Behavior Analysis
An LLM-based RAG framework that retrieves method-level Java code snippets to explain and detect malicious behavior in Android apps.
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Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior?
A five-model benchmark on a self-built malware dataset shows the smallest strong model, Phi-4-mini, outperforming 7-8B models, contradicting the paper's own 'larger models win' framing.
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