Abstract: In recent years,large model-based network security vulnerability risk awareness has gradually become a research hotspot.However,existing methods still suffer from slow response speed and low semantic quality in terms of intelligent operational efficiency and fine-grained information perception.To address these issues,this paper proposes a lightweight network security vulnerability risk awareness method based on Retrieval-Augmented Generation(RAG).Firstly,by constructing a cross-domain knowledge base and integrating a cross-domain vectorization algorithm,efficient vectorization of network vulnerability information is achieved.Then,a multi-objective retrieval algorithm is designed to adaptively extract highly relevant fine-grained vulnerability information from the knowledge base.Finally,by incorporating metadata-based secondary enhancement and a local large model,intelligent vulnerability risk awareness response is completed.Experimental results show that the proposed method significantly outperforms existing approaches in both semantic quality and operational efficiency,enabling rapid responses to network security risks and providing high-quality countermeasures,fully meeting the demands of intelligent network security operations.

Key words: Vulnerability risk awareness, Retrieval-augmented generation, Penetration testing, Network security, Protection stra-tegy