Demo #5: Disclosing the Pringles Syndrome in Tesla FSD Vehicles

Zhisheng Hu (Baidu), Shengjian Guo (Baidu) and Kang Li (Baidu)

In this demo, we disclose a potential bug in the Tesla Full Self-Driving (FSD) software. A vulnerable FSD vehicle can be deterministically tricked to run a red light. Attackers can cause a victim vehicle to behave in such ways without tampering or interfering with any sensors or physically accessing the vehicle. We infer that such behavior is caused by Tesla FSD’s decision system failing to take latest perception signals once it enters a specific mode. We call such problematic behavior Pringles Syndrome. Our study on multiple other autonomous driving implementations shows that this failed state update is a common failure pattern that specially needs attentions in autonomous driving software tests and developments.

Paper

View More Papers

Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators

Shijia Li (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Chunfu Jia (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Pengda Qiu (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data…

DrawnApart: A Deep-Learning Enhanced GPU Fingerprinting Technique

Naif Mehanna (University of Lille, CNRS, Inria), Tomer Laor (Ben-Gurion University of the Negev)

Shout-Out for PASS (Platform for Auto-driving Safety and Security)

Zhisheng Hu (Baidu Security)

Demo #13: Attacking LiDAR Semantic Segmentation in Autonomous Driving

Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)