Imani N. S. Munyaka (University of California, San Diego), Daniel A Delgado, Juan Gilbert, Jaime Ruiz, Patrick Traynor (University of Florida)

Telephone carriers and third-party developers have created technical solutions to detect and notify consumers of spam calls. The goal of this technology is to help users make decisions about incoming calls and reduce the negative effects of spam calls on finances and daily life. Although useful, this technology has varying accuracy due to technical limitations. In this study, we conduct design interviews, a call response diary study, and an MTurk survey (N=143) to explore the relationship between warning accuracy and callee decision-making for incoming calls. Our results suggest that previous call experience can lead to incomplete mental models of how Caller ID works. Additionally, we find that false alarms and missed detection do not impact call response but can influence user expectations of the call. Since adversaries can use mismatched expectations to their advantage, we recommend using warning design characteristics that align with user expectations under detection accuracy constraints.

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Stacking up the LLM Risks: Applied Machine Learning Security

Dr. Gary McGraw, Berryville Institute of Machine Learning

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EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

Yan Long (University of Michigan), Qinhong Jiang (Zhejiang University), Chen Yan (Zhejiang University), Tobias Alam (University of Michigan), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University), Kevin Fu (Northeastern University)

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File Hijacking Vulnerability: The Elephant in the Room

Chendong Yu (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Yang Xiao (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Jie Lu (Institute of Computing Technology of the Chinese Academy of Sciences), Yuekang…

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MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models

Qi Pang (Carnegie Mellon University), Yuanyuan Yuan (HKUST), Shuai Wang (HKUST)

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