@inproceedings{yang-etal-2025-fraud,
    title = "Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of {LLM} Against Augmented Fraud and Phishing Inducements",
    author = "Yang, Shu  and
      Zhu, Shenzhe  and
      Wu, Zeyu  and
      Wang, Keyu  and
      Yao, Junchi  and
      Wu, Junchao  and
      Hu, Lijie  and
      Li, Mengdi  and
      Wong, Derek F.  and
      Wang, Di",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.226/",
    pages = "4374--4420",
    ISBN = "979-8-89176-256-5",
    abstract = "With the increasing integration of large language models (LLMs) into real-world applications such as finance, e-commerce, and recommendation systems, their susceptibility to misinformation and adversarial manipulation poses significant risks. Existing fraud detection benchmarks primarily focus on single-turn classification tasks, failing to capture the dynamic nature of real-world fraud attempts. To address this gap, we introduce Fraud-R1, a challenging bilingual benchmark designed to assess LLMs' ability to resist fraud and phishing attacks across five key fraud categories: Fraudulent Services, Impersonation, Phishing Scams, Fake Job Postings, and Online Relationships, covering subclasses. Our dataset comprises manually curated fraud cases from social media, news, phishing scam records, and prior fraud datasets."
}