Our paper, “Re(gEx|DoS)Eval: Evaluating Generated Regular Expressions and their Proneness to DoS Attack, got accepted for the 46th International Conference on Software Engineering - New Ideas and Emerging Results Track. In this work, we presented a novel dataset and framework to evaluate LLM-generated RegEx and ReDoS vulnerability.
The Re(gEx|DoS)Eval framework includes a dataset of 762 regex descriptions (prompts) from real users, refined prompts with examples, and a robust set of tests. We introduce the pass@k and vulnerable@k metrics to evaluate the generated regexes based on the functional correctness and proneness of ReDoS attacks. Moreover, we demonstrate the Re(gEx|DoS)Eval with three large language model families, i.e., T5, Phi, and GPT-3, and describe the plan for the future extension of this framework.
@inproceedings{siddiq2024regexeval,
author={Siddiq, Mohammed Latif and Zhang, Jiahao and Roney, Lindsay and Santos, Joanna C. S.},
booktitle={Proceedings of the 46th International Conference on Software Engineering, NIER Track (ICSE-NIER '24)},
title={Re(gEx|DoS)Eval: Evaluating Generated Regular Expressions and their Proneness to DoS Attacks},
year={2024},
doi = {10.1145/3639476.3639757}
}
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"SALLM: Security Assessment of Generated Code" accepted at ASYDE 2024 (ASE Workshop)
Posted on 07 Sep 2024Paper (13) Research (13) Tool (2) Llm (10) Dataset (2) Qualitative-analysis (1) Survey (1)