Tool-Guided Retrieval-Augmented Repair for Securing LLM-Generated C Code

• URL: http://arxiv.org/abs/2607.21641v1
• Date: Tue, 21 Jul 2026 19:01:59 GMT
• Title: Tool-Guided Retrieval-Augmented Repair for Securing LLM-Generated C Code
• Abstract summary: Large language models can generate C code from natural-language descriptions, but resulting programs often contain security vulnerabilities and compilation errors.<n>This work investigates how feedback and retrieval improve reliability of LLM-generated C code.
• Score: 1.3682073278404534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models can generate C code from natural-language descriptions, but resulting programs often contain security vulnerabilities and compilation errors, posing risks for embedded and resource-constrained systems. This work investigates how feedback and retrieval improve reliability of LLM-generated C code. We present an analysis-and-repair workflow that combines compilation diagnostics, CodeQL static analysis, and KLEE symbolic execution with retrieval of prior repair patterns for iterative refinement. Evaluated on 5,000 C programming tasks exercising embedded relevant vulnerabilities, baseline models show substantial reliability gaps, with compilation failure rates up to 46% and security defect rates up to 49%. Our approach improves both metrics. For CodeLlama 7B, security defect rates decrease from 49% to 19% and total CodeQL errors drop from 15,088 to 2,463 (83.7%). For DeepSeek Coder 1.3B, compilation failures are reduced from 42% to 22% and security defects from 35% to 15%. These results show that integrating lightweight analysis tools can improve the safety of LLM-generated code for embedded development.

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