Shilin Xiao (Zhejiang University), Wenjun Zhu (Zhejiang University), Yan Jiang (Zhejiang University), Kai Wang (Zhejiang University), Peiwang Wang (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Sensors are fundamental to cyber-physical systems (CPS), enabling perception and control by transducing physical stimuli into digital measurements. However, despite growing research on physical attacks on sensors, our understanding of sensor hardware vulnerabilities remains fragmented due to the ad-hoc nature of this field. Moreover, the infinite attack signal space further complicates threat abstraction and defense. To address this gap, we propose a systematization framework, termed sensor out-of-band (OOB) vulnerabilities, that for the first time provides a comprehensive abstraction for sensor attack surfaces based on underlying physical principles. We adopt a bottom-up systematization methodology that analyzes OOB vulnerabilities across three levels. At the component level, we identify the physical principles and limitations that contribute to OOB vulnerabilities. At the sensor level, we categorize known attacks and evaluate their practicality. At the system level, we analyze how CPS features such as sensor fusion, closed-loop control, and intelligent perception impact the exposure and mitigation of OOB threats. Our findings offer a foundational understanding of sensor hardware security and provide guidance and future directions for sensor designers, security researchers, and system developers aiming to build more secure sensors and CPS.

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Binbin Tu (School of Cyber Science and Technology, Shandong University; State Key Laboratory of Cryptography and Digital Economy Security, Shandong University), Boyudong Zhu (School of Cyber Science and Technology, Shandong University; State Key Laboratory of Cryptography and Digital Economy Security, Shandong University), Yang Cao (School of Cyber Science and Technology, Shandong University; State Key Laboratory…

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QNBAD: Quantum Noise-induced Backdoor Attacks against Zero Noise Extrapolation

Cheng Chu (Indiana University Bloomington), Qian Lou (University of Central Florida), Fan Chen (Indiana University Bloomington), Lei Jiang (Indiana University Bloomington)

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MUTATO: Enhancing Fuzz Drivers with Adaptive API Option Mutation

Shuangxiang Kan (University of New South Wales), Xiao Cheng (Macquarie University), Yuekang Li (University of New South Wales)

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