Michele Marazzi, Stefano Longari, Michele Carminati, Stefano Zanero (Politecnico di Milano)

ZOOX AutoDriving Security Award Runner-up!

With the increasing interest in autonomous vehicles (AVs), ensuring their safety and security is becoming crucial. The introduction of advanced features has increased the need for various interfaces to communicate with the external world, creating new potential attack vectors that attackers can exploit to alter sensor data. LiDAR sensors are widely employed to support autonomous driving features and generate point cloud data used by ADAS to 3D map the vehicle’s surroundings. Tampering attacks on LiDAR-generated data can compromise the vehicle’s functionalities and seriously threaten passengers and other road users. Existing approaches to LiDAR data tampering detection show security flaws and can be bypassed by attackers through design vulnerabilities. This paper proposes a novel approach for tampering detection of LiDAR-generated data in AVs, employing a watermarking technique. We validate our approach through experiments to prove its feasibility in realworld time-constrained scenarios and its efficacy in detecting LiDAR tampering attacks. Our approach performs better when compared to the current state-of-the-art LiDAR watermarking techniques while addressing critical issues related to watermark security and imperceptibility.

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PANDORA: Jailbreak GPTs by Retrieval Augmented Generation Poisoning

Gelei Deng, Yi Liu (Nanyang Technological University), Yuekang Li (The University of New South Wales), Wang Kailong(Huazhong University of Science and Technology), Tianwei Zhang, Yang Liu (Nanyang Technological University)

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Security Attacks to the Name Management Protocol in Vehicular...

Sharika Kumar (The Ohio State University), Imtiaz Karim, Elisa Bertino (Purdue University), Anish Arora (Ohio State University)

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More Lightweight, yet Stronger: Revisiting OSCORE’s Replay Protection

Konrad-Felix Krentz (Uppsala University), Thiemo Voigt (Uppsala University, RISE Computer Science)

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MadRadar: A Black-Box Physical Layer Attack Framework on mmWave...

David Hunt (Duke University), Kristen Angell (Duke University), Zhenzhou Qi (Duke University), Tingjun Chen (Duke University), Miroslav Pajic (Duke University)

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