Scientific AI systems require domain-aware robustness validation

AI has become an essential tool in many areas of scientific discovery. However, its robustness, particularly in high-stakes scientific applications, remains insufficiently understood. While adversarial vulnerabilities have been widely studied in image recognition, their implications in scientific disciplines, where physical constraints and numerical stability are critical, have not been thoroughly explored. This work presents a systematic evaluation of AI model fragility across five scientific domains: climate modeling, quantum chemistry, fluid dynamics, quantum chromodynamics, and wireless communications. We demonstrate that small, structured perturbations can destabilize predictions in all of these domains, even in models that achieve high accuracy under normal conditions. Furthermore, the failure patterns vary across domains, suggesting that these vulnerabilities are domain specific. Our findings underscore the importance of assessing AI robustness in scientific contexts and offer preliminary insights into how to design more resilient and reliable AI systems for scientific applications.