The rapid ascent of quantum computing and its integration with machine learning introduces an entirely new frontier for cybersecurity research. This paper addresses the critical and cutting-edge security challenge of safeguarding quantum machine learning (QML) models against insidious data manipulation attacks. We present a novel cross-domain adversarial strategy that leverages an intrinsic understanding of quantum data representations to inject highly effective corruptions into QML training datasets. Unlike traditional methods, our approach demonstrates robust efficacy even in the presence of realistic quantum noise. Through rigorous experimental validation across diverse quantum architectures, we showcase the profound detrimental impact of this vulnerability on QML model performance, underscoring the urgent need for robust defenses in the nascent quantum computing landscape. This work provides foundational insights into securing the next generation of intelligent systems.
