When Fuzzing Meets LLMs: Challenges and Opportunities
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913329009655808 |
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| author | Jiang, Yu Liang, Jie Ma, Fuchen Chen, Yuanliang Zhou, Chijin Shen, Yuheng Wu, Zhiyong Fu, Jingzhou Wang, Mingzhe Li, ShanShan Zhang, Quan |
| author_facet | Jiang, Yu Liang, Jie Ma, Fuchen Chen, Yuanliang Zhou, Chijin Shen, Yuheng Wu, Zhiyong Fu, Jingzhou Wang, Mingzhe Li, ShanShan Zhang, Quan |
| contents | Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from top-tier conferences, confirming that these challenges are widespread. As a remedy, we propose some actionable recommendations to help improve applying LLM in Fuzzing and conduct preliminary evaluations on DBMS fuzzing. The results demonstrate that our recommendations effectively address the identified challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16297 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When Fuzzing Meets LLMs: Challenges and Opportunities Jiang, Yu Liang, Jie Ma, Fuchen Chen, Yuanliang Zhou, Chijin Shen, Yuheng Wu, Zhiyong Fu, Jingzhou Wang, Mingzhe Li, ShanShan Zhang, Quan Software Engineering Artificial Intelligence Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from top-tier conferences, confirming that these challenges are widespread. As a remedy, we propose some actionable recommendations to help improve applying LLM in Fuzzing and conduct preliminary evaluations on DBMS fuzzing. The results demonstrate that our recommendations effectively address the identified challenges. |
| title | When Fuzzing Meets LLMs: Challenges and Opportunities |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2404.16297 |