Enhancing Communication and Data Transmission Security in RAG Using Large Language Models

Abstract

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more useful information and generating accurate responses. This paper explores RAG's architecture and applications, combining generator and retriever models to access and utilize vast external data repositories. While RAG holds significant promise for various Natural Language Processing (NLP) processes like dialogue generation, summarization, and question answering, it also presents unique security challenges that must be addressed to ensure system integrity and reliability. RAG systems face several security threats, including data poisoning, model manipulation, privacy leakage, biased information retrieval, and harmful outputs generation. Generally, in the traditional RAG application, security threat is one of the major concerns. To tighten the security system and enhance the efficiency of the model on processing more complex data this paper outlines key strategies for securing RAG-based applications to mitigate these risks paper outlines key strategies for securing RAG-based applications to mitigate these risks. Ensuring data security through filtering, sanitization, and provenance tracking can prevent data poisoning and enhance the quality of external knowledge sources. Strengthening model security via adversarial training, input validation, and anomaly detection improves resilience against manipulative attacks. Implementing output monitoring and f…