Soroush Karami (University of Illinois at Chicago), Panagiotis Ilia (University of Illinois at Chicago), Jason Polakis (University of Illinois at Chicago)

Service workers are a powerful technology supported by all major modern browsers that can improve users' browsing experience by offering capabilities similar to those of native applications. While they are gaining significant traction in the developer community, they have not received much scrutiny from security researchers. In this paper, we explore the capabilities and inner workings of service workers and conduct the first comprehensive large-scale study of their API use in the wild. Subsequently, we show how attackers can exploit the strategic placement of service workers for history-sniffing in most major browsers, including Chrome and Firefox. We demonstrate two novel history-sniffing attacks that exploit the lack of appropriate isolation in these browsers, including a non-destructive cache-based version. Next, we present a series of use cases that illustrate how our techniques enable privacy-invasive attacks that can infer sensitive application-level information, such as a user's social graph. We have disclosed our techniques to all vulnerable vendors, prompting the Chromium team to explore a redesign of their site isolation mechanisms for defending against our attacks. We also propose a countermeasure that can be incorporated by websites to protect their users, and develop a tool that streamlines its deployment, thus facilitating adoption at a large scale. Overall, our work presents a cautionary tale on the severe risks of browsers deploying new features without an in-depth evaluation of their security and privacy implications.

View More Papers

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

Read More

SpecTaint: Speculative Taint Analysis for Discovering Spectre Gadgets

Zhenxiao Qi (UC Riverside), Qian Feng (Baidu USA), Yueqiang Cheng (NIO Security Research), Mengjia Yan (MIT), Peng Li (ByteDance), Heng Yin (UC Riverside), Tao Wei (Ant Group)

Read More

Demo #9: Attacking Multi-Sensor Fusion based Localization in High-Level...

Junjie Shen, Jun Yeon Won, Zeyuan Chen and Qi Alfred Chen (UC Irvine)

Read More

Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

Read More