SecRAG: A Graph-Enhanced RAG Framework with Dynamic Prompt for Cybersecurity Applications
Yu Qiao, Lun Li, Feng Cheng, Jie Zhang, Jin Gao, Hongsong Zhu · 2025
In this paper, we introduce SecRAG, a novel Retrieval-Augmented Generation (RAG) system specifically designed for cybersecurity applications. SecRAG tackles fundamental challenges in context precision and domain-specific terminology through a dual-pronged approach: 1) a data augmentation method optimized for cybersecurity contexts, particularly addressing the RAG system's numerical information sensitivity limitations; and 2) an enhanced dual-level retrieval architecture that integrates graph-based knowledge representation and adaptive text indexing, incorporating a dynamic prompt weighting mechanism based on dual similarity metrics (δ1, δ2) for query relationship and output coherence assessment, to enable comprehensive information discovery. Experimental evaluation on the SecEval benchmark demonstrates that SecRAG achieves stantial improvements over conventional RAG implementations, with a 30% increase in vulnerability node recall rates and reduction in temporal confusion rate to 1.1%. The system has shown particular strengths in specialized areas, achieving overall accuracy rate of 72.02% on the SecEval benchmarks. Our framework effectively addresses the unique challenges of cybersecurity-focused RAG systems, particularly in handling precise numeric attributes and maintaining contextual coherence in multi-turn dialogues.