RESCUE: Retrieval Augmented Secure Code Generation

• URL: http://arxiv.org/abs/2510.18204v1
• Date: Tue, 21 Oct 2025 01:13:03 GMT
• Title: RESCUE: Retrieval Augmented Secure Code Generation
• Authors: Jiahao Shi, Tianyi Zhang,
• Abstract summary: We propose RESCUE, a new framework for secure code generation with two key innovations.<n>First, we propose a hybrid knowledge base construction method that combines LLM-assisted cluster-then-summarize distillation with program slicing.<n>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.
• Score: 5.001448044530164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 RESCUE on four benchmarks and compared it with five state-of-the-art secure code generation methods on six LLMs. The results demonstrate that 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 RESCUE.

Related papers

• Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision [3.983997834693767]
  Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development.<n>LLMs cannot readily adapt to newly discovered vulnerabilities or changing security standards without retraining.<n>We present a principled approach to trustworthy code generation by design that operates as an inference-time safety mechanism.
  arXiv  Detail & Related papers  (2026-03-02T06:06:34Z)
• Learning to Generate Secure Code via Token-Level Rewards [11.539519023515021]
  Large language models (LLMs) have demonstrated strong capabilities in code generation, yet they remain prone to producing security vulnerabilities.<n>We propose Vul2Safe, a new secure code generation framework that leverages self-reflection to construct high-confidence repair pairs from real-world vulnerabilities.<n>We also introduce SRCode, a novel training framework that pioneers the use of token-level rewards in reinforcement learning for code security.
  arXiv  Detail & Related papers  (2026-02-26T12:57:27Z)
• Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model [60.60587869092729]
  Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment.<n>We propose SecCoderX, an online reinforcement learning framework for functionality-preserving secure code generation.
  arXiv  Detail & Related papers  (2026-02-07T07:42:07Z)
• RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
  arXiv  Detail & Related papers  (2026-01-30T08:29:01Z)
• Automated Safety Benchmarking: A Multi-agent Pipeline for LVLMs [61.01470415470677]
  Large vision-language models (LVLMs) exhibit remarkable capabilities in cross-modal tasks but face significant safety challenges.<n>Existing benchmarks are hindered by their labor-intensive construction process, static complexity, and limited discriminative power.<n>We propose VLSafetyBencher, the first automated system for LVLM safety benchmarking.
  arXiv  Detail & Related papers  (2026-01-27T11:51:30Z)
• A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code [49.009041488527544]
  A.S.E is a repository-level evaluation benchmark for assessing the security of AI-generated code.<n>Current large language models (LLMs) still struggle with secure coding.<n>A larger reasoning budget does not necessarily lead to better code generation.
  arXiv  Detail & Related papers  (2025-08-25T15:11:11Z)
• A Comprehensive Study of LLM Secure Code Generation [19.82291066720634]
  Previous research primarily relies on a single static analyzer, CodeQL, to detect vulnerabilities in generated code.<n>We apply both security inspection and functionality validation to the same generated code and evaluate these two aspects together.<n>Our study reveals that existing techniques often compromise the functionality of generated code to enhance security.
  arXiv  Detail & Related papers  (2025-03-18T20:12:50Z)
• CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation [20.72188827088484]
  Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing.<n> detecting vulnerabilities in functionally correct code is more challenging, especially for developers with limited security knowledge.<n>We introduce CWEval, a novel outcome-driven evaluation framework designed to enhance the evaluation of secure code generation by LLMs.
  arXiv  Detail & Related papers  (2025-01-14T15:27:01Z)
• SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [58.29510889419971]
  Existing benchmarks for evaluating the security risks and capabilities of code-generating large language models (LLMs) face several key limitations.<n>We introduce a general and scalable benchmark construction framework that begins with manually validated, high-quality seed examples and expands them via targeted mutations.<n>Applying this framework to Python, C/C++, and Java, we build SeCodePLT, a dataset of more than 5.9k samples spanning 44 CWE-based risk categories and three security capabilities.
  arXiv  Detail & Related papers  (2024-10-14T21:17:22Z)
• HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
  Large language models (LLMs) have shown great potential for automatic code generation. Recent studies highlight that many LLM-generated code contains serious security vulnerabilities. We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
  arXiv  Detail & Related papers  (2024-09-10T12:01:43Z)
• Developing Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models? [2.089112028396727]
  This study explores whether Large Language Models can produce safe, unbiased outputs without sacrificing knowledge or comprehension.<n>We introduce the Safe and Responsible Large Language Model (textbfSR$_textLLM$)<n>Experiments on our specialized dataset and out-of-distribution test sets reveal that textbfSR$_textLLM$ effectively reduces biases while preserving knowledge integrity.
  arXiv  Detail & Related papers  (2024-04-01T18:10:05Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.