ARTFEED — Contemporary Art Intelligence

LLM-Generated C Code Security Improved by Tool-Guided Repair

ai-technology · 2026-07-27

A novel workflow for analysis and repair integrates compilation diagnostics, CodeQL static analysis, and KLEE symbolic execution, along with the retrieval of previous repair patterns to iteratively enhance C code generated by LLMs. In a study involving 5,000 C programming tasks that included relevant vulnerabilities, baseline models exhibited compilation failure rates as high as 46% and security defect rates reaching 49%. For CodeLlama 7B, the security defect rate improved significantly, decreasing from 49% to 19%, while total CodeQL errors were reduced from 15,088 to 2,463, marking an 83.7% decline. Compilation failures were also minimized for DeepSeek Coder 1.3B. This research is detailed in arXiv:2607.21641.

Key facts

  • arXiv:2607.21641
  • Workflow uses compilation diagnostics, CodeQL, KLEE, and retrieval
  • Evaluated on 5,000 C programming tasks
  • Baseline compilation failure rates up to 46%
  • Baseline security defect rates up to 49%
  • CodeLlama 7B: security defects from 49% to 19%
  • CodeLlama 7B: CodeQL errors from 15,088 to 2,463 (83.7%)
  • DeepSeek Coder 1.3B compilation failures reduced

Entities

Institutions

  • arXiv
  • CodeQL
  • KLEE
  • CodeLlama
  • DeepSeek Coder

Sources