Vision-Language-Action (VLA) models have emerged as promising solutions for robotic manipulation, yet their robustness to real-world physical variations remains critically underexplored. To bridge this gap, we propose Eva-VLA, the first unified framework to systematically evaluate the robustness of VLA models by formulating uncontrollable physical variations as continuous optimization problems. Specifically, our framework addresses two fundamental challenges in VLA models' physical robustness evaluation: 1) how to systematically characterize diverse physical perturbations encountered in real-world deployment while maintaining reproducibility, and 2) how to efficiently discover worst-case scenarios without incurring prohibitive real-world data collection costs. To tackle the first challenge, we decouple real-world variations into three key dimensions: 3D object transformations that affect spatial reasoning, illumination changes that challenge visual perception, and adversarial regions that disrupt scene understanding. For the second challenge, we introduce a continuous black-box optimization mechanism that maps these perturbations into a continuous parameter space, enabling the systematic exploration of worst-case scenarios. Extensive experiments validate the effectiveness of our approach. Notably, OpenVLA exhibits an average failure rate of over 90% across three physical variations on the LIBERO-Long task, exposing critical systemic fragilities. Furthermore, applying the generated worst-case scenarios during adversarial training quantifiably increases model robustness, validating the effectiveness of this approach. Our evaluation exposes the gap between laboratory and real-world conditions, while the Eva-VLA framework can serve as an effective data augmentation method to enhance the resilience of robotic manipulation systems.