Soheil Khodayari (CISPA Helmholtz Center for Information Security), Kai Glauber (Saarland University), Giancarlo Pellegrino (CISPA Helmholtz Center for Information Security)

Open redirects are one of the oldest threats to web applications, allowing attackers to reroute users to malicious websites by exploiting a web application's redirection mechanism. The recent shift towards client-side task offloading has introduced JavaScript-based redirections, formerly handled server-side, thereby posing additional security risks to open redirections. In this paper, we re-assess the significance of open redirect vulnerabilities by focusing on client-side redirections, which despite their importance, have been largely understudied by the community due to open redirect's long-standing low impact. To address this gap, we introduce a static-dynamic system, STORK, designed to extract vulnerability indicators for open redirects. Applying STORK to the Tranco top 10K sites, we conduct a large-scale measurement, uncovering 20.8K open redirect vulnerabilities across 623 sites and compiling a catalog of 184 vulnerability indicators. Afterwards, we use our indicators to mine vulnerabilities from snapshots of live webpages, Google search and Internet Archive, identifying additionally 326 vulnerable sites, including Google WebLight and DoubleClick. Then, we explore the extent to which their exploitation can lead to more critical threats, quantifying the impact of client-side open redirections in the wild. Our study finds that over 11.5% of the open redirect vulnerabilities across 38% of the affected sites could be escalated to XSS, CSRF and information leakage, including popular sites like Adobe, WebNovel, TP-Link, and UDN, which is alarming. Finally, we review and evaluate the adoption of mitigation techniques against open redirections.

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Daniel J. Bernstein (University of Illinois at Chicago and Academia Sinica), Tanja Lange (Eindhoven University of Technology amd Academia Sinica), Jonathan Levin (Academia Sinica and Eindhoven University of Technology), Bo-Yin Yang (Academia Sinica)

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TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents

Chen Gong (University of Vriginia), Kecen Li (Chinese Academy of Sciences), Jin Yao (University of Virginia), Tianhao Wang (University of Virginia)

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Yuxi Wu (Georgia Institute of Technology and Northeastern University), Jacob Logas (Georgia Institute of Technology), Devansh Ponda (Georgia Institute of Technology), Julia Haines (Google), Jiaming Li (Google), Jeffrey Nichols (Apple), W. Keith Edwards (Georgia Institute of Technology), Sauvik Das (Carnegie Mellon University)

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Guanlong Wu (Southern University of Science and Technology), Zheng Zhang (ByteDance Inc.), Yao Zhang (ByteDance Inc.), Weili Wang (Southern University of Science and Technolog), Jianyu Niu (Southern University of Science and Technolog), Ye Wu (ByteDance Inc.), Yinqian Zhang (Southern University of Science and Technology (SUSTech))

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