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

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.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2607.21641 [cs.SE]

(or arXiv:2607.21641v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2607.21641 Focus to learn more arXiv-issued DOI via DataCite

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