Elijah Bouma-Sims (Carnegie Mellon University), Lily Klucinec (Carnegie Mellon University), Mandy Lanyon (Carnegie Mellon University), Julie Downs (Carnegie Mellon University), Lorrie Faith Cranor (Carnegie Mellon University)

Fraudsters often use the promise of free goods as a lure for victims who are convinced to complete online tasks but ultimately receive nothing. Despite much work characterizing these "giveaway scams," no human subjects research has investigated how users interact with them or what factors impact victimization. We conducted a scenario-based experiment with a sample of American teenagers (n = 85) and adult crowd workers (n = 205) in order to investigate how users reason about and interact with giveaway scams advertised in YouTube videos and to determine whether teens are more susceptible than adults. We found that most participants recognized the fraudulent nature of the videos, with only 9.2% believing the scam videos offered legitimate deals. Teenagers did not fall victim to the scams more frequently than adults but reported more experience searching for terms that could lead to victimization. This study is among the first to compare the interactions of adult and teenage users with internet fraud and sheds light on an understudied area of social engineering.

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Be Careful of What You Embed: Demystifying OLE Vulnerabilities

Yunpeng Tian (Huazhong University of Science and Technology), Feng Dong (Huazhong University of Science and Technology), Haoyi Liu (Huazhong University of Science and Technology), Meng Xu (University of Waterloo), Zhiniang Peng (Huazhong University of Science and Technology; Sangfor Technologies Inc.), Zesen Ye (Sangfor Technologies Inc.), Shenghui Li (Huazhong University of Science and Technology), Xiapu Luo…

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Understanding Influences on SMS Phishing Detection: User Behavior, Demographics,...

Daniel Timko (California State University San Marcos), Daniel Hernandez Castillo (California State University San Marcos), Muhammad Lutfor Rahman (California State University San Marcos)

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Secure Transformer Inference Made Non-interactive

Jiawen Zhang (Zhejiang University), Xinpeng Yang (Zhejiang University), Lipeng He (University of Waterloo), Kejia Chen (Zhejiang University), Wen-jie Lu (Zhejiang University), Yinghao Wang (Zhejiang University), Xiaoyang Hou (Zhejiang University), Jian Liu (Zhejiang University), Kui Ren (Zhejiang University), Xiaohu Yang (Zhejiang University)

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