Masashi Fukunaga (MitsubishiElectric), Takeshi Sugawara (The University of Electro-Communications)

Integrity of sensor measurement is crucial for safe and reliable autonomous driving, and researchers are actively studying physical-world injection attacks against light detection and ranging (LiDAR). Conventional work focused on object/obstacle detectors, and its impact on LiDAR-based simultaneous localization and mapping (SLAM) has been an open research problem. Addressing the issue, we evaluate the robustness of a scan-matching SLAM algorithm in the simulation environment based on the attacker capability characterized by indoor and outdoor physical experiments. Our attack is based on Sato et al.’s asynchronous random spoofing attack that penetrates randomization countermeasures in modern LiDARs. The attack is effective with fake points injected behind the victim vehicle and potentially evades detection-based countermeasures working within the range of object detectors. We discover that mapping is susceptible toward the z-axis, the direction perpendicular to the ground, because feature points are scarce either in the sky or on the road. The attack results in significant changes in the map, such as a downhill converted into an uphill. The false map induces errors to the self-position estimation on the x-y plane in each frame, which accumulates over time. In our experiment, after making laser injection for 5 meters (i.e. 1 second), the victim SLAM’s self-position begins and continues to diverge from the reality, resulting in the 5m shift to the right after running 125 meters. The false map and self-position significantly affect the motion planning algorithm, too; the planned trajectory changes by 3◦ with which the victim vehicle will enter the opposite lane after running 35 meters. Finally, we discuss possible mitigations against the proposed attack.

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Large Language Model guided Protocol Fuzzing

Ruijie Meng (National University of Singapore, Singapore), Martin Mirchev (National University of Singapore), Marcel Böhme (MPI-SP, Germany and Monash University, Australia), Abhik Roychoudhury (National University of Singapore)

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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On Requirements and Concepts for TT&C Link Key Management

Christoph Bader (Airbus Defence & Space GmbH)

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WIP: Towards Practical LiDAR Spoofing Attack against Vehicles Driving...

Ryo Suzuki (Keio University), Takami Sato (University of California, Irvine), Yuki Hayakawa, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

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