Title: Rescue: Retrieval Augmented Secure Code Generation
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: secure code generation, retrieval augmented generation

Abstract: Despite recent advances, Large Language Models (LLMs) still generate vulnerable code. 
Retrieval-Augmented Generation (RAG) has the potential to enhance LLMs for secure code generation by incorporating external security knowledge.  
However, the conventional RAG design struggles with the noise of raw security-related documents, and existing retrieval methods overlook the significant security semantics implicitly embedded in task descriptions.
To address these issues, we propose \textsc{Rescue}, a new RAG framework for secure code generation with two key innovations. 
First, we propose a hybrid knowledge base construction method that combines LLM-assisted cluster-then-summarize distillation with program slicing, producing both high-level security guidelines and concise, security-focused code examples. Second, we design a hierarchical multi-faceted retrieval to traverse the constructed knowledge base from top to bottom and integrates multiple security-critical facts at each hierarchical level, ensuring comprehensive and accurate retrieval. 
We evaluated \textsc{Rescue} on four benchmarks and compared it with five state-of-the-art secure code generation methods on six LLMs. The results demonstrate that \textsc{Rescue} improves the SecurePass@1 metric by an average of 4.8 points, establishing a new state-of-the-art performance for security. Furthermore, we performed in-depth analysis and ablation studies to rigorously validate the effectiveness of individual components in \textsc{Rescue}. Our code is available at \url{https://anonymous.4open.science/r/RESCUE}.