That Doesn't Go There: Attacks on Shared State in Multi-User Augmented Reality Applications
Authors:
Carter Slocum,
Yicheng Zhang,
Erfan Shayegani,
Pedram Zaree,
Nael Abu-Ghazaleh,
Jiasi Chen
Abstract:
Augmented Reality (AR) is expected to become a pervasive component in enabling shared virtual experiences. In order to facilitate collaboration among multiple users, it is crucial for multi-user AR applications to establish a consensus on the "shared state" of the virtual world and its augmentations, through which they interact within augmented reality spaces. Current methods to create and access…
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Augmented Reality (AR) is expected to become a pervasive component in enabling shared virtual experiences. In order to facilitate collaboration among multiple users, it is crucial for multi-user AR applications to establish a consensus on the "shared state" of the virtual world and its augmentations, through which they interact within augmented reality spaces. Current methods to create and access shared state collect sensor data from devices (e.g., camera images), process them, and integrate them into the shared state. However, this process introduces new vulnerabilities and opportunities for attacks. Maliciously writing false data to "poison" the shared state is a major concern for the security of the downstream victims that depend on it. Another type of vulnerability arises when reading the shared state; by providing false inputs, an attacker can view hologram augmentations at locations they are not allowed to access. In this work, we demonstrate a series of novel attacks on multiple AR frameworks with shared states, focusing on three publicly-accessible frameworks. We show that these frameworks, while using different underlying implementations, scopes, and mechanisms to read from and write to the shared state, have shared vulnerability to a unified threat model. Our evaluation of these state-of-art AR applications demonstrates reliable attacks both on updating and accessing shared state across the different systems. To defend against such threats, we discuss a number of potential mitigation strategies that can help enhance the security of multi-user AR applications.
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Submitted 8 March, 2024; v1 submitted 17 August, 2023;
originally announced August 2023.