GPU in the Blind Spot: Overlooked Security Risks in Transportation

• URL: http://arxiv.org/abs/2508.01995v1
• Date: Mon, 04 Aug 2025 02:25:43 GMT
• Title: GPU in the Blind Spot: Overlooked Security Risks in Transportation
• Authors: Sefatun-Noor Puspa, Mashrur Chowdhury,
• Abstract summary: This paper highlights GPU security as a critical blind spot in transportation cybersecurity.<n>To support this concern, it also presents a case study showing the impact of stealthy unauthorized crypto miners on critical AI workloads.
• Score: 3.3296812191509786
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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