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arXiv · 2606.19983

A Measurement Study of Cryptographic Misuse in Embodied AI Mobile Applications

Junchao Li·Xuelei Wang·Yuhang Huang·Qi Wang·Boyang Ma·Xuelong Dai·Minghui Xu·Yue Zhang
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Abstract

Embodied AI (EAI) mobile applications are evolving from auxiliary user interfaces into active control-path components, directly linking mobile-side cryptographic security to cyber-physical trust. Despite this shift, existing security research predominantly focuses on embodied AI devices and cloud infrastructures, leaving the mobile control layer largely unexplored as a critical attack surface. To bridge this gap, we present the first large-scale measurement study of cryptographic misuse within the EAI mobile ecosystem. We construct EAIAppZoo, a benchmark of 507 real-world applications across six EAI domains, and employ an automated semantic-aware analysis pipeline to measure the prevalence and characteristics of five major cryptographic failure modes. Our measurement yields 12,975 misuse findings (with an evaluated precision of 80.74\%), revealing that these cryptographic failures are driven by EAI-specific engineering constraints rather than random developer errors. We uncover structural security trade-offs: latency-sensitive control paths systematically weaken transport protection, while the heavy reliance on offline device provisioning and legacy IoT SDKs exacerbates the local hardcoding of authentication credentials. Through real-world case studies, we demonstrate how these mobile-side cryptographic flaws bypass nominal network protections, enabling adversaries to intercept command channels and hijack the physical control of EAI entities. Ultimately, our findings highlight that mobile applications have become a fragile, yet overlooked, cryptographic trust boundary in cyber-physical systems.

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Junchao Li, Xuelei Wang, Yuhang Huang, Qi Wang, Boyang Ma, Xuelong Dai, Minghui Xu, Yue Zhang. 2026-06-18. A Measurement Study of Cryptographic Misuse in Embodied AI Mobile Applications. https://arxiv.org/abs/2606.19983

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