Reethika Ramesh (University of Michigan), Leonid Evdokimov (Independent), Diwen Xue (University of Michigan), Roya Ensafi (University of Michigan)

Use of Virtual Private Networks (VPNs) has been growing rapidly due to increased public awareness of online risks to privacy and security. This growth has fueled the VPN ecosystem to expand into a multi-billion dollar industry that sees a frequent influx of new VPN providers. Nevertheless, the VPN ecosystem remains severely understudied, and the limited research concerning VPNs has relied on laborious manual processes. There is a need for a solution which empowers researchers and average users to investigate their VPN providers.

In this work, we present VPNalyzer, a system that enables systematic, semi-automated investigation into the VPN ecosystem. We develop a cross-platform tool with a comprehensive measurement test suite containing 15 measurements that test for aspects of service, security and privacy essentials, misconfigurations, and leakages. Using the VPNalyzer tool, we conduct the largest investigation into 80 desktop VPNs.

Our investigation reveals several previously unreported findings highlighting key issues and implementation shortcomings in the VPN ecosystem. We find evidence of traffic leaks during tunnel failure in 26 VPN providers, which seriously risk exposing sensitive user data. We are the first to measure and detect DNS leaks during tunnel failure, which we observe in eight providers. Overall, we find a majority of providers lack IPv6 support, and five even leak IPv6 traffic to the user's ISP. We observe that adoption of practices we consider security and privacy essentials is not uniform across VPN providers. Multiple providers share underlying infrastructure, and 29 providers use third-party, public DNS services. Alarmingly, 10 VPN providers leak traffic even in their most secure configuration, with six leaking data even with a "kill switch" feature enabled. Our results highlight the effectiveness of VPNalyzer in finding issues even in the most popular VPN providers. Consumer Reports used VPNalyzer in their efforts to create data-driven recommendations for their users.

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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)

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A S M Rizvi (University of Southern California/Information Sciences Institute) and John Heidemann (University of Southern California/Information Sciences Institute)

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Ahmed Salem (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security)

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ScriptChecker: To Tame Third-party Script Execution With Task Capabilities

Wu Luo (Peking University), Xuhua Ding (Singapore Management University), Pengfei Wu (School of Computing, National University of Singapore), Xiaolei Zhang (Peking University), Qingni Shen (Peking University), Zhonghai Wu (Peking University)

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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)