Large language models-driven fuzzing: A systematic survey

Y. Wan

doi:10.1016/j.engappai.2026.115126

2026-05-17

PRE

DOI：10.1016/j.engappai.2026.115126

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摘要

En 中文

• 对LLM驱动的模糊测试这一新兴领域的系统综述进行了介绍。 • 提出了一种新的分类方法，基于模糊测试生命周期对技术进行分类。 • 对三种主要策略进行了深入分析：微调、提示工程和混合方法。 • 识别了核心挑战，包括性能瓶颈和语义差距。

Keyword:

LLM-driven fuzzing

fuzzing lifecycle

prompt engineering

fine-tuning

hybrid methods

期刊

Engineering Applications of Artificial Intelligence

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论文数:

5.8K

被引数:

3.5W

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