Online scams have become increasingly prevalent, with scammers using psychological techniques (PTs) to manipulate victims. While existing research has developed benchmarks to study scammer behaviors, these benchmarks do not adequately reflect the PTs observed in real-world scams. To fill this gap, we introduce PsyScam, a benchmark designed to systematically capture and evaluate PTs embedded in real-world scam reports. In particular, PsyScam bridges psychology and real-world cyber security analysis through collecting a wide range of scam reports from six public platforms and grounding its annotations in well-established cognitive and psychological theories. We further demonstrate PsyScam's utility through three downstream tasks: PT classification, scam completion, and scam augmentation. Experimental results show that PsyScam presents significant challenges to existing models in both detecting and generating scam content based on the PTs used by real-world scammers. Our code and dataset are available at:this https URL.
View on arXiv@article{ma2025_2505.15017,
title={ PsyScam: A Benchmark for Psychological Techniques in Real-World Scams },
author={ Shang Ma and Tianyi Ma and Jiahao Liu and Wei Song and Zhenkai Liang and Xusheng Xiao and Yanfang Ye },
journal={arXiv preprint arXiv:2505.15017},
year={ 2025 }
}