Prompting Instability: An Empirical Study of LLM Robustness in Code Vulnerability Detection

Shuo Han, Tao Tan, Yuantian Miao, Xiao Chen, Nan Sun. Prompting Instability: An Empirical Study of LLM Robustness in Code Vulnerability Detection. In Miaomiao Liu 0001, Xin Yu 0002, Chang Xu, Yiliao Song, editors, AI 2025: Advances in Artificial Intelligence - 38th Australasian Joint Conference on Artificial Intelligence, AI 2025, Canberra, ACT, Australia, December 1-5, 2025, Proceedings, Part I. Volume 16370 of Lecture Notes in Computer Science, pages 233-245, Springer, 2025. [doi]

@inproceedings{HanTMCS25,
  title = {Prompting Instability: An Empirical Study of LLM Robustness in Code Vulnerability Detection},
  author = {Shuo Han and Tao Tan and Yuantian Miao and Xiao Chen and Nan Sun},
  year = {2025},
  doi = {10.1007/978-981-95-4969-6_18},
  url = {https://doi.org/10.1007/978-981-95-4969-6_18},
  researchr = {https://researchr.org/publication/HanTMCS25},
  cites = {0},
  citedby = {0},
  pages = {233-245},
  booktitle = {AI 2025: Advances in Artificial Intelligence - 38th Australasian Joint Conference on Artificial Intelligence, AI 2025, Canberra, ACT, Australia, December 1-5, 2025, Proceedings, Part I},
  editor = {Miaomiao Liu 0001 and Xin Yu 0002 and Chang Xu and Yiliao Song},
  volume = {16370},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  isbn = {978-981-95-4969-6},
}