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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TWINFUZZ: Differential Testing of Video Hardware Acceleration Stacks

Matteo Leonelli (CISPA Helmholtz Center for Information Security), Addison Crump (CISPA Helmholtz Center for Information Security), Meng Wang (CISPA Helmholtz Center for Information Security), Florian Bauckholt (CISPA Helmholtz Center for Information Security), Keno Hassler (CISPA Helmholtz Center for Information Security), Ali Abbasi (CISPA Helmholtz Center for Information Security), Thorsten Holz (CISPA Helmholtz Center for Information…

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LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

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Inspecting Compiler Optimizations on Mixed Boolean Arithmetic Obfuscation

Rachael Little, Dongpeng Xu (University of New Hampshire)

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Work-in-Progress: Uncovering Dark Patterns: A Longitudinal Study of Cookie...

Zihan Qu (Johns Hopkins University), Xinyi Qu (University College London), Xin Shen, Zhen Liang, and Jianjia Yu (Johns Hopkins University)

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