Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements

We introduce Fraud-R1 , a benchmark designed to evaluate LLMs' ability to defend against internet fraud and phishing in dynamic, real-world scenarios.Fraud-R1 comprises 8,564 fraud cases sourced from phishing scams, fake job postings, social media, and news, categorized into 5 major fraud types.Unlike previous benchmarks, Fraud-R1 introduces a multiround evaluation pipeline to assess LLMs' resistance to fraud at different stages, including credibility building, urgency creation, and emotional manipulation.Furthermore, we evaluate 15 LLMs under two settings: (i) Helpful-Assistant, where the LLM provides general decision-making assistance, and (ii) Role-play, where the model assumes a specific persona, widely used in real-world agent-based interactions.Our evaluation reveals the significant challenges in defending against fraud and phishing inducement, especially in role-play settings and fake job postings.Additionally, we observe a substantial performance gap between Chinese and English, underscoring the need for improved multilingual fraud detection capabilities.The source code and dataset for this benchmark is publicly available at: