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.

View More Papers

Silence False Alarms: Identifying Anti-Reentrancy Patterns on Ethereum to...

Qiyang Song (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Heqing Huang (Institute of Information Engineering, Chinese Academy of Sciences), Xiaoqi Jia (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Yuanbo Xie (Institute of Information…

Read More

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian...

Kaiyuan Zhang (Purdue University), Siyuan Cheng (Purdue University), Guangyu Shen (Purdue University), Bruno Ribeiro (Purdue University), Shengwei An (Purdue University), Pin-Yu Chen (IBM Research AI), Xiangyu Zhang (Purdue University), Ninghui Li (Purdue University)

Read More

EvoCrawl: Exploring Web Application Code and State using Evolutionary...

Xiangyu Guo (University of Toronto), Akshay Kawlay (University of Toronto), Eric Liu (University of Toronto), David Lie (University of Toronto)

Read More

Incorporating Gradients to Rules: Towards Lightweight, Adaptive Provenance-based Intrusion...

Lingzhi Wang (Northwestern University), Xiangmin Shen (Northwestern University), Weijian Li (Northwestern University), Zhenyuan LI (Zhejiang University), R. Sekar (Stony Brook University), Han Liu (Northwestern University), Yan Chen (Northwestern University)

Read More