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.

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Demo #8: Identifying Drones Based on Visual Tokens

Ben Nassi (Ben-Gurion University of the Negev), Elad Feldman (Ben-Gurion University of the Negev), Aviel Levy (Ben-Gurion University of the Negev), Yaron Pirutin (Ben-Gurion University of the Negev), Asaf Shabtai (Ben-Gurion University of the Negev), Ryusuke Masuoka (Fujitsu System Integration Laboratories) and Yuval Elovici (Ben-Gurion University of the Negev)

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Demo #12: Too Afraid to Drive: Systematic Discovery of...

Ziwen Wan (UC Irvine), Junjie Shen (UC Irvine), Jalen Chuang (UC Irvine), Xin Xia (UCLA), Joshua Garcia (UC Irvine), Jiaqi Ma (UCLA) and Qi Alfred Chen (UC Irvine)

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Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

Gonzalo De La Torre Parra (University of the Incarnate Word, TX, USA), Luis Selvera (Secure AI and Autonomy Lab, The University of Texas at San Antonio, TX, USA), Joseph Khoury (The Cyber Center For Security and Analytics, University of Texas at San Antonio, TX, USA), Hector Irizarry (Raytheon, USA), Elias Bou-Harb (The Cyber Center For…

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