用大模型自动生成高质量测试用例种子，提升代码模糊测试效果
SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing
- 通过大模型生成搜索词，自动收集互联网公开文件构建测试语料
- 生成的语料在多种程序上表现接近人工精选语料，优于简单收集
- 适合安全研究人员和开发者快速搭建高效模糊测试流程
我们提出SeedAIchemy，一种由大语言模型驱动的自动化语料生成工具，帮助开发者更有效地实施模糊测试。该工具包含五个模块，分别采用不同方法从互联网获取公开文件。其中四个模块利用大语言模型工作流，设计旨在最大化语料质量的搜索关键词。在多种目标程序和库上，SeedAIchemy生成的语料表现显著优于基础语料，且与人工精心筛选的语料相当。
原文摘要 · Abstract (English)
We introduce SeedAIchemy, an automated LLM-driven corpus generation tool that makes it easier for developers to implement fuzzing effectively. SeedAIchemy consists of five modules which implement different approaches at collecting publicly available files from the internet. Four of the five modules use large language model (LLM) workflows to construct search terms designed to maximize corpus quality. Corpora generated by SeedAIchemy perform significantly better than a naive corpus and similarly to a manually-curated corpus on a diverse range of target programs and libraries.
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