XGen-Q: An Explainable Domain-Adaptive LLM Framework with Retrieval-Augmented Generation for Software Security

2025/10/21 by Hamed Jelodar, Jelodar, Hamed, Mohammad Meymani +5 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Digital and Cyber Forensics #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Software Engineering Research

paper · pdf · doi:10.48550/arxiv.2510.19006

openalex publication_date 2025/10/21 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28

Abstract

Generative AI and large language models (LLMs) have shown strong capabilities in code understanding, but their use in cybersecurity, particularly for malware detection and analysis, remains limited. Existing detection systems often fail to generalize to obfuscated or previously unseen threats, underscoring the need for more adaptable and explainable models. To address this challenge, we introduce XGen-Q, a domain-adapted LLM built on the Qwen-Coder architecture and pretrained on a large-scale corpus of over one million malware samples, spanning both source and assembly code. XGen-Q uses a multi-stage prompt strategy combined with retrieval-augmented generation (RAG) to deliver reliable malware identification and detailed forensic reporting, even in the presence of complex code obfuscation. To further enhance generalization, we design a training pipeline that systematically exposes the model to diverse obfuscation patterns. Experimental results show that XGen-Q achieves significantly lower perplexity than competitive baselines and exhibits strong performance on novel malware samples, demonstrating the promise of LLM-based approaches for interpretable and robust malware analysis.

Citations

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• Large Language Models
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Cited by

• Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior?

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• LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection
• Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering
• Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches
• Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities
• LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models