Mitigating GPU Side-Channels via Integrated Monitoring and Response

Graphics processing units (GPUs) are increasingly employed in security-sensitive domains, making side-channel attacks on shader execution a pressing threat. We present an integrated shader execution framework incorporating real-time performance monitoring, machine learning anomaly detection, and architecture-aware response primitives to detect and mitigate GPU side-channel vulnerabilities with minimal overhead reliably. A shader execution monitors non-invasively profiles hardware counters to extract execution features, passed to an isolation forest classifier for attack detection within 50ms. Transparent mitigations like cache flushing, warp throttling, and shuffle masking are triggered to disrupt identified side channels by leveraging GPU architectural capabilities. Evaluations against simulated attacks demonstrate that the framework reduces key leakage by 4-5x and achieves over 80% attack disruption for a 400ms attack at under 5% performance cost. The integrated GPU-specific security solution significantly enhances real-world shader execution protection against side-channel risks. This paper present an integrated shader execution framework that leverages advanced monitoring, anomaly detection, and finely tuned response mechanisms.