Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU Systems
Sankha Baran Dutta, Hoda Naghibijouybari, Arjun Gupta, Nael, Abu-Ghazaleh, Andres Marquez, Kevin Barker

TL;DR
This paper demonstrates that multi-GPU systems, specifically Nvidia's DGX machines, are vulnerable to covert and side channel attacks, revealing potential security risks in shared high-performance computing environments.
Contribution
It is the first to analyze and demonstrate microarchitectural vulnerabilities in multi-GPU systems, developing covert and side channel attacks that can fingerprint applications.
Findings
Achieved a covert channel bandwidth of 3.95 MB/s between GPUs.
Developed a remote cache attack to recover cache behavior of other workloads.
Showed high accuracy in fingerprinting applications via side channel attacks.
Abstract
The deep learning revolution has been enabled in large part by GPUs, and more recently accelerators, which make it possible to carry out computationally demanding training and inference in acceptable times. As the size of machine learning networks and workloads continues to increase, multi-GPU machines have emerged as an important platform offered on High Performance Computing and cloud data centers. As these machines are shared between multiple users, it becomes increasingly important to protect applications against potential attacks. In this paper, we explore the vulnerability of Nvidia's DGX multi-GPU machines to covert and side channel attacks. These machines consist of a number of discrete GPUs that are interconnected through a combination of custom interconnect (NVLink) and PCIe connections. We reverse engineer the cache hierarchy and show that it is possible for an attacker on…
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Taxonomy
TopicsSecurity and Verification in Computing · Adversarial Robustness in Machine Learning · Diamond and Carbon-based Materials Research
