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.

View More Papers

Rediscovering Method Confusion in Proposed Security Fixes for Bluetooth

Maximilian von Tschirschnitz (Technical University of Munich), Ludwig Peuckert (Technical University of Munich), Moritz Buhl (Technical University of Munich), Jens Grossklags (Technical University of Munich)

Read More

SNITCH: Leveraging IP Geolocation for Active VPN Detection

Tomer Schwartz (Data and Security Laboratory Fujitsu Research of Europe Ltd), Ofir Manor (Data and Security Laboratory Fujitsu Research of Europe Ltd), Andikan Otung (Data and Security Laboratory Fujitsu Research of Europe Ltd)

Read More

TZ-DATASHIELD: Automated Data Protection for Embedded Systems via Data-Flow-Based...

Zelun Kong (University of Texas at Dallas), Minkyung Park (University of Texas at Dallas), Le Guan (University of Georgia), Ning Zhang (Washington University in St. Louis), Chung Hwan Kim (University of Texas at Dallas)

Read More

How Different Tokenization Algorithms Impact LLMs and Transformer Models...

Ahmed Mostafa, Raisul Arefin Nahid, Samuel Mulder (Auburn University)

Read More