Xinyi Xie (Shanghai Fudan Microelectronics Group Co., Ltd.), Kun Jiang (Shanghai Fudan Microelectronics Group Co., Ltd.), Rui Dai (Shanghai Fudan Microelectronics Group Co., Ltd.), Jun Lu (Shanghai Fudan Microelectronics Group Co., Ltd.), Lihui Wang (Shanghai Fudan Microelectronics Group Co., Ltd.), Qing Li (State Key Laboratory of ASIC & System, Fudan University), Jun Yu (State Key Laboratory of ASIC & System, Fudan University)

Tesla Model 3 has equipped with Phone Keys and Key Cards in addition to traditional key fobs for better driving experiences. These new features allow a driver to enter and start the vehicle without using a mechanical key through a wireless authentication process between the vehicle and the key. Unlike the requirements of swiping against the car for Key Cards, the Tesla mobile app’s Phone Key feature can unlock a Model 3 while your smartphone is still in a pocket or bag.

In this paper, we performed a detailed security analysis aiming at Tesla keys, especially for Key Cards and Phone Keys. Starting with reverse engineering the mobile application and sniffing the communication data, we reestablished pairing and authentication protocols and analyzed their potential issues. Missing the certificate verification allows an unofficial Key Card to work as an official one. Using these third-party products may lead to serious security problems. Also, the weaknesses of the current protocol lead to a man-in-the-middle (MitM) attack through a Bluetooth channel. The MitM attack is an improved relay attack breaking the security of the authentication procedures for Phone Keys. We also developed an App named TESmLA installed on customized Android devices to complete the proof-of-concept. The attackers can break into Tesla Model 3 and drive it away without the awareness of the car owner. Our results bring into question the security of Passive Keyless Entry and Start (PKES) and Bluetooth implementations in security-critical applications. To mitigate the security problems, we discussed the corresponding countermeasures and feasible secure scheme in the future.

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Augmented Reality’s Potential for Identifying and Mitigating Home Privacy...

Stefany Cruz (Northwestern University), Logan Danek (Northwestern University), Shinan Liu (University of Chicago), Christopher Kraemer (Georgia Institute of Technology), Zixin Wang (Zhejiang University), Nick Feamster (University of Chicago), Danny Yuxing Huang (New York University), Yaxing Yao (University of Maryland), Josiah Hester (Georgia Institute of Technology)

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Enhanced Vehicular Roll-Jam Attack using a Known Noise Source

Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

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Muslum Ozgur Ozmen (Purdue University), Ruoyu Song (Purdue University), Habiba Farrukh (Purdue University), Z. Berkay Celik (Purdue University)

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An Exploratory study of Malicious Link Posting on Social...

Muhammad Hassan, Mahnoor Jameel, Masooda Bashir (University of Illinois at Urbana Champaign)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)