Large Language Models (LLMs) have achieved remarkable progress in understanding and generation tasks, yet they remain highly susceptible to adversarial prompt attacks that bypass safety safeguards and induce the generation of harmful content. Existing pre-generation and post-generation defense methods typically rely on intent recognition, surface-level features, or external classifiers, rendering them vulnerable to evasion via subtle prompt perturbations while incurring substantial computational overhead.

Large language models (LLMs) provide effective solutions in various application scenarios, with the support of retrieval-augmented generation (RAG). However, developers face challenges in integrating LLM and RAG into software systems, due to lacking interface specifications, various requirements from software context, and complicated system management. In this paper, we have conducted a comprehensive study of 100 open-source applications that incorporate LLMs with RAG support, and identified 18 defect patterns. Our study reveals that 77 % of these applications contain more than three types of integration defects that degrade software functionality, efficiency, and security. Guided by our study, we propose systematic guidelines for resolving these defects in software life cycle. We also construct an open-source defect library HYDRANGEA [1].