As augmented reality (AR) becomes increasingly integrated into everyday life, ensuring the safety and trustworthiness of its virtual content is critical. Our research addresses the risks of task-detrimental AR content, particularly that which obstructs critical information or subtly manipulates user perception. We developed two systems, ViDDAR and VIM-Sense, to detect such attacks using vision-language models (VLMs) and multimodal reasoning modules. Building on this foundation, we propose three future directions: automated, perceptually aligned quality assessment of virtual content; detection of multimodal attacks; and adaptation of VLMs for efficient and user-centered deployment on AR devices. Overall, our work aims to establish a scalable, human-aligned framework for safeguarding AR experiences and seeks feedback on perceptual modeling, multimodal AR content implementation, and lightweight model adaptation.