Quantum Neural Networks (QNNs) represent a promising frontier in quantum machine learning, offering potential advantages in terms of expressivity and computational efficiency. However, their rapid development has outpaced critical attention to their security. In this work, we position QNN security as a foundational research challenge and present a comprehensive threat model that includes input encoding, circuit compilation, execution, and deployment. We introduce a taxonomy of emerging quantum-specific attack classes, including dataset poisoning, circuit tampering, side-channel leakage, multi-tenant interference, and federated learning threats. Our analysis highlights key gaps, including the lack of quantum-native robustness metrics, hardware variability, and cross-layer vulnerabilities. To address these issues, we outline strategic directions for developing secure-by-design QNN architectures, emphasizing formal verification, quantum-aware defenses, and adversarial benchmarking. This work lays the groundwork for a systematic approach to building trustworthy and resilient quantum learning systems.