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<p>The widespread adoption of deep learning (DL) libraries has raised concerns about their reliability and security. While prior works leveraged large language models (LLMs) to generate test programs for DL library APIs, the hardcoded program behaviors and low code validity rates render them impractical for real-world testing. To address these challenges, we propose FD-FACTORY, a fully automated framework that leverages LLMs to generate fuzz drivers for DL API testing. The fuzz driver programs accept mutated inputs from fuzzing engines to achieve effective code analysis. Inspired by the modular design of industrial production lines, FD-FACTORY decomposes the generation process into eight distinct stages: <i>Preparation, Initial Fuzz Driver Generation, Early Stop Checks, Verification, Issue Diagnosis, Decision Making, Repair Loop, and Deployment</i>. Each stage is handled by dedicated agents or tools to enhance construction efficiency. Experimental results demonstrate that FD-FACTORY achieves 73.67% and 65.33% success rates in generating fuzz drivers for PyTorch and TensorFlow, producing an improvement of 34.66 to <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(-\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>&#xa0;54.66% than existing approaches. In addition, FD-FACTORY provides more comprehensive coverage tracking by supporting both Python and native C/C<Emphasis FontCategory="NonProportional">++</Emphasis> code. It achieves a total coverage of 308,351 lines on PyTorch and 528,427 lines on TensorFlow, substantially surpassing the results reported by previous approaches. Unlike prior approaches relying on repeated interactions with the LLM servers throughout the entire testing process, our framework confines the use of LLMs strictly to the fuzz driver generation stages before deployment. Once generated, the fuzz drivers can be reused without further LLM involvement, thereby enhancing the practicality and sustainability of LLM-assisted fuzzing in real-world scenarios.</p>

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Automating fuzz driver generation for deep learning libraries with large language models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月4日

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  • Tianming Zheng,
  • Fanchao Meng,
  • Ping Yi,
  • Yue Wu

摘要

The widespread adoption of deep learning (DL) libraries has raised concerns about their reliability and security. While prior works leveraged large language models (LLMs) to generate test programs for DL library APIs, the hardcoded program behaviors and low code validity rates render them impractical for real-world testing. To address these challenges, we propose FD-FACTORY, a fully automated framework that leverages LLMs to generate fuzz drivers for DL API testing. The fuzz driver programs accept mutated inputs from fuzzing engines to achieve effective code analysis. Inspired by the modular design of industrial production lines, FD-FACTORY decomposes the generation process into eight distinct stages: Preparation, Initial Fuzz Driver Generation, Early Stop Checks, Verification, Issue Diagnosis, Decision Making, Repair Loop, and Deployment. Each stage is handled by dedicated agents or tools to enhance construction efficiency. Experimental results demonstrate that FD-FACTORY achieves 73.67% and 65.33% success rates in generating fuzz drivers for PyTorch and TensorFlow, producing an improvement of 34.66 to \(-\) -  54.66% than existing approaches. In addition, FD-FACTORY provides more comprehensive coverage tracking by supporting both Python and native C/C++ code. It achieves a total coverage of 308,351 lines on PyTorch and 528,427 lines on TensorFlow, substantially surpassing the results reported by previous approaches. Unlike prior approaches relying on repeated interactions with the LLM servers throughout the entire testing process, our framework confines the use of LLMs strictly to the fuzz driver generation stages before deployment. Once generated, the fuzz drivers can be reused without further LLM involvement, thereby enhancing the practicality and sustainability of LLM-assisted fuzzing in real-world scenarios.