Speed Kills: Exploring Confused Deputy Attacks Through Edge AI Accelerators

Abstract

AI Accelerators (AIAs) are specialized hardware, such as Tensor Processing Units (TPUs), that enable optimal and efficient execution of AI applications and on-device inference. The growing demand for AI applications has led to the widespread adoption of AIAs on edge and embedded devices. Unlike applications, AIAs are not bound by Operating System (OS) restrictions and have limited visibility into Application Processor (AP) security mechanisms (e.g., kernel versus application memory and process isolation). This semantic gap can lead to confused deputy vulnerabilities, where an AIA can be tricked by a malicious application into performing privileged operations on its behalf. In this paper, we conduct the first in-depth study of Confused Deputy Attacks (CDAs) using AIAs. We design DeputyHunt, a Large Language Model (LLM)-assisted framework to extract CDA-relevant information for a given AIA through a combination of dynamic and static analysis. We use this information to explore the feasibility of CDAs on seven different AIAs from popular vendors, including Google, NVIDIA, Hailo, Texas Instruments, NXP, AWS, and Rockchip. Our analysis reveals that CDAs are feasible on six out of the seven AIAs, impacting over 128 System-on-Chips (SoCs) and over 100 million devices. Our findings highlight critical security risks posed by AIAs to system security. Our work has been acknowledged by the corresponding vendors and assigned CVE-2025-66425. We propose an on-demand validation defense against CDAs, and our evaluation on the Gem5-salam simulator shows that the average runtime overhead of our mechanism is significantly lower (~15% versus ~56%) than that of traditional defenses.