Security Rule Embedding and Static-Analysis-Guided Repair for LLM-Based Intelligent Multimedia UI Code Generation

Abstract

Large language models (LLMs) can rapidly generate web user interface (UI) code for intelligent multimedia applications, but the resulting code may contain avoidable client-side security weaknesses. This paper presents a security governance framework that combines pre-generation rule embedding, ESLint-based static analysis, structured feedback, and LLM-based repair. The framework was evaluated through a controlled pilot ablation study involving 10 multimedia UI requirements, five experimental groups, and three repeated runs, producing 150 requests. Across all groups, 132 outputs passed structural and task-specific validity checks, while 18 were recorded as generation failures rather than being treated as secure code. For the Full Framework, the matched-pair end-to-end static finding reduction rate was 95.16% (SD = 4.76 percentage points), the valid output rate was 90.00%, and the static-secure valid output rate was $\mathbf{7 6. 6 7 \%}(\mathbf{S D}=$ 11.55 percentage points). Holm-adjusted paired Wilcoxon tests showed statistically significant differences, with lower observed final finding counts for the Full Framework than for Vanilla, Prompt Only, and the variant without structured static feedback. Repair-enabled configurations increased the average runtime from approximately 51 s to 79 s. These results concern static candidate findings and output validity; they do not replace server-side controls or exhaustive dynamic security testing.