arXiv subjects

Aravind Kumar Machiry

Publications and source records attributed to Aravind Kumar Machiry.

2 recordsLinked to original sources

arXiv2026

Speed Kills: Exploring Confused Deputy Attacks Through Edge AI Accelerators

AI Accelerator (AIA) are specialized hardware e.g., Tensor Processing Unit (TPU), 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 or embedded devices on Edge or 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 vs. application memory, process isolation). This semantic gap can lead to confused deputy vulnerabilities, i.e., AIA can be tricked by a malicious application to perform privileged operations on their behalf. In this paper, we conducted the first in-depth study of Confused Deputy Attacks (CDAs) using AIA. 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 used this information to explore the feasibility of CDA on seven different AIAs from popular vendors, i.e., Google, NVIDIA, Hailo, Texas Instruments, NXP, AWS, and Rockchip. Our analysis revealed that CDA is 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 AIA on system security. Our work has been acknowledged by the corresponding vendors and assigned the CVE-2025-66425. We propose an on-demand validation defense against CDA, and evaluation on the Gem5- salam simulator shows that it incurs minimal runtime overhead (i.e., ~15%).

cs.CR↗

arXiv2025

LEMIX: Enabling Testing of Embedded Applications as Linux Applications (Extended Report)

Dynamic analysis, through rehosting, is an important capability for security assessment in embedded systems software. Existing rehosting techniques aim to provide high-fidelity execution by accurately emulating hardware and peripheral interactions. However, these techniques face challenges in adoption due to the increasing number of available peripherals and the complexities involved in designing emulation models for diverse hardware. Additionally, contrary to the prevailing belief that guides existing works, our analysis of reported bugs shows that high-fidelity execution is not required to expose most bugs in embedded software. Our key hypothesis is that security vulnerabilities are more likely to arise at higher abstraction levels. To substantiate our hypothesis, we introduce LEMIX, a framework enabling dynamic analysis of embedded applications by rehosting them as x86 Linux applications decoupled from hardware dependencies. Enabling embedded applications to run natively on Linux facilitates security analysis using available techniques and takes advantage of the powerful hardware available on the Linux platform for higher testing throughput. We develop various techniques to address the challenges involved in converting embedded applications to Linux applications. We evaluated LEMIX on 18 real-world embedded applications across four RTOSes and found 21 new bugs in 12 of the applications and all 4 of the RTOS kernels. We report that LEMIX is superior to existing state-of-the-art techniques both in terms of code coverage (~2x more coverage) and bug detection (18 more bugs).

cs.CR↗