GPU in the Blind Spot: Overlooked Security Risks in Transportation

TL;DR

This paper highlights overlooked security vulnerabilities in transportation GPUs, demonstrating how unauthorized crypto mining can degrade performance and proposing telemetry-based detection methods to address this critical blind spot.

Contribution

It identifies GPU security as a neglected issue in transportation systems and presents a case study with a detection framework using telemetry data for unauthorized crypto mining.

Findings

Crypto mining significantly reduces GPU frame rate and increases power consumption.

Telemetry-based classifiers can accurately detect GPU misuse.

GPU security is a critical but overlooked aspect of transportation cybersecurity.

Abstract

Graphics processing units (GPUs) are becoming an essential part of the intelligent transportation system (ITS) for enabling video-based and artificial intelligence (AI) based applications. GPUs provide high-throughput and energy-efficient computing for tasks like sensor fusion and roadside video analytics. However, these GPUs are one of the most unmonitored components in terms of security. This makes them vulnerable to cyber and hardware attacks, including unauthorized crypto mining. This paper highlights GPU security as a critical blind spot in transportation cybersecurity. To support this concern, it also presents a case study showing the impact of stealthy unauthorized crypto miners on critical AI workloads, along with a detection strategy. We used a YOLOv8-based video processing pipeline running on an RTX 2060 GPU for the case study. A multi-streaming application was executed while a T-Rex crypto miner ran in the background. We monitored how the miner degraded GPU performance by reducing the frame rate and increasing power consumption, which could be a serious concern for GPUs operating in autonomous vehicles or battery-powered edge devices. We observed measurable impacts using GPU telemetry (nvidia-smi) and Nsight Compute profiling, where frame rate dropped by 50 percent, and power usage increased by up to 90%. To detect, we trained lightweight classifiers using extracted telemetry features. All models achieved high accuracy, precision, recall, and F1-score. This paper raises urgent awareness about GPU observability gaps in ITS and offers a replicable framework for detecting GPU misuse through on-device telemetry.