Fengchen Yang (Zhejiang University), Wenze Cui (Zhejiang University), Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Fluorescent lamps are almost everywhere for electric lighting in daily life, across private and public scenarios. Our study uncovers a new electromagnetic interference (EMI) attack surface that these light sources are actually able to manipulate nearby IoT devices in a contactless way. Different from previous EMI attempts requiring a specialized metal antenna as the emission source, which can easily alert victims, we introduce LightAntenna that leverages unaltered everyday fluorescent lamps to launch concealed EMI attacks. To understand why and how fluorescent lamps can be exploited as malicious antennas, we systematically characterize the rationale of EMI emission from fluorescent lamps and identify their capabilities and limits in terms of intensity and frequency response. Moreover, we carefully design a covert method of injecting high-frequency signals into the fluorescent tube via power line transmission. In this way, LightAntenna can realize controllable EMI attacks even across rooms and at a distance of up to 20 m. Our extensive experiments demonstrate the generality, practicality, tunability, and remote attack capability of LightAntenna, which successfully interferes with various types of sensors and IoT devices. In summary, our study provides a comprehensive analysis of the LightAntenna mechanism and proposes defensive strategies to mitigate this emerging attack surface.

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Siniel: Distributed Privacy-Preserving zkSNARK

Yunbo Yang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Yuejia Cheng (Shanghai DeCareer Consulting Co., Ltd), Kailun Wang (Beijing Jiaotong University), Xiaoguo Li (College of Computer Science, Chongqing University), Jianfei Sun (School of Computing and Information Systems, Singapore Management University), Jiachen Shen (Shanghai Key Laboratory of Trustworthy Computing, East China Normal…

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NodeMedic-FINE: Automatic Detection and Exploit Synthesis for Node.js Vulnerabilities

Darion Cassel (Carnegie Mellon University), Nuno Sabino (IST & CMU), Min-Chien Hsu (Carnegie Mellon University), Ruben Martins (Carnegie Mellon University), Limin Jia (Carnegie Mellon University)

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CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models

Rui Zeng (Zhejiang University), Xi Chen (Zhejiang University), Yuwen Pu (Zhejiang University), Xuhong Zhang (Zhejiang University), Tianyu Du (Zhejiang University), Shouling Ji (Zhejiang University)

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Scale-MIA: A Scalable Model Inversion Attack against Secure Federated...

Shanghao Shi (Virginia Tech), Ning Wang (University of South Florida), Yang Xiao (University of Kentucky), Chaoyu Zhang (Virginia Tech), Yi Shi (Virginia Tech), Y. Thomas Hou (Virginia Polytechnic Institute and State University), Wenjing Lou (Virginia Polytechnic Institute and State University)

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