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CAESAR: Mixed Reality Search-and-Rescue Platform for Cognitive Attack Evaluation

Mingyu Zhu, Jiangong Chen, Yu Liu, Hillmer Chona, Zifan Zhou, Jianan Jiang, Jonathan E. Dodge, Feng Qian, Mahdi Imani, Tian Lan, Gang Tan, Bin Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27654 · cited by 0 works (OpenAlex) · open-access PDF

Abstract

Mixed Reality (MR) enables immersive training by tightly coupling virtual content with the physical environment and supporting embodied interaction. In safety-critical collaborative tasks such as search-and-rescue, however, users rely heavily on virtual elements for perception, coordination, and decision-making, making them vulnerable to cognitive attacks that subtly distort visual feedback, virtual content, or system synchronization. While prior MR security research has examined individual attack mechanisms, evaluations are typically conducted in isolated or single-user settings, and there is a lack of standardized collaborative testbeds for systematic adversarial experimentation. In this paper, we develop CAESAR, an end-to-end multi-user MR framework for systematic evaluation of cognitive attacks in collaborative search-and-rescue training. CAESAR features a modular architecture that integrates four classes of cognitive attacks within a synchronized collaborative scenario and incorporates a multimodal data collection pipeline that captures behavioral, performance, and subjective measures under adversarial conditions. We conduct a controlled user study with an in-depth analysis of the latency-based attack, demonstrating measurable effects on task performance and user behavior while characterizing users' subjective experience under adversarial conditions.

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Topics: Human–computer interaction, Computer science, Virtual reality, Task (project management), Adversarial system, Cognition

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