Tejas Kannan (University of Chicago), Synthia Qia Wang (University of Chicago), Max Sunog (University of Chicago), Abraham Bueno de Mesquita (University of Chicago Laboratory Schools), Nick Feamster (University of Chicago), Henry Hoffmann (University of Chicago)

Smart Televisions (TVs) are internet-connected TVs that support video streaming applications and web browsers. Users enter information into Smart TVs through on-screen virtual keyboards. These keyboards require users to navigate between keys with directional commands from a remote controller. Given the extensive functionality of Smart TVs, users type sensitive information (e.g., passwords) into these devices, making keystroke privacy necessary. This work develops and demonstrates a new side-channel attack that exposes keystrokes from the audio of two popular Smart TVs: Apple and Samsung. This side-channel attack exploits how Smart TVs make different sounds when selecting a key, moving the cursor, and deleting a character. These properties allow an attacker to extract the number of cursor movements between selections from the TV's audio. Our attack uses this extracted information to identify the likeliest typed strings. Against realistic users, the attack finds up to 33.33% of credit card details and 60.19% of common passwords within 100 guesses. This vulnerability has been acknowledged by Samsung and highlights how Smart TVs must better protect sensitive data.

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GTrans: Graph Transformer-Based Obfuscation-resilient Binary Code Similarity Detection

Yun Zhang (Hunan University), Yuling Liu (Hunan University), Ge Cheng (Xiangtan University), Bo Ou (Hunan University)

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Aligning Confidential Computing with Cloud-native ML Platforms

Angelo Ruocco, Chris Porter, Claudio Carvalho, Daniele Buono, Derren Dunn, Hubertus Franke, James Bottomley, Marcio Silva, Mengmei Ye, Niteesh Dubey, Tobin Feldman-Fitzthum (IBM Research)

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Maginot Line: Assessing a New Cross-app Threat to PII-as-Factor...

Fannv He (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China), Yan Jia (DISSec, College of Cyber Science, Nankai University, China), Jiayu Zhao (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China), Yue Fang (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China),…

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Predictive Context-sensitive Fuzzing

Pietro Borrello (Sapienza University of Rome), Andrea Fioraldi (EURECOM), Daniele Cono D'Elia (Sapienza University of Rome), Davide Balzarotti (Eurecom), Leonardo Querzoni (Sapienza University of Rome), Cristiano Giuffrida (Vrije Universiteit Amsterdam)

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