Person search aims to simultaneously detect and re-identify a query person within an entire scene.While existing studies have made significant progress in achieving superior performance on clean datasets, the challenge of robustness under various corruptions remains largely unexplored.However, the lack of environments for analyzing corruption robustness presents a challenge, as extensive collection of new person images attempting to cover numerous corruption scenarios inevitably introduces privacy concerns.In this context, we construct the environments for analyzing corruption robustness using existing publicly available data, and introduce two benchmarks: CUHK-SYSU-C and PRW-C.Previous studies on corruption have been conducted independently for single tasks such as re-identification and detection.However, recent advancements in person search adopt an end-to-end multi-task learning framework that processes the entire scene as input, unlike the combination of single tasks. This raises the question of whether independent achievements can ensure corruption robustness for person search.Our findings reveal that merely combining independent, robust detection and re-identification models is not sufficient for achieving robust person search. We further investigate the vulnerability of the detection and representation stages to corruption and explore its impact on both foreground and background areas.Based on these insights, we propose a foreground-aware augmentation and regularization method to enhance the robustness of person search models.Supported by our comprehensive robustness analysis and evaluation framework our benchmarks provide, our proposed technique substantially improves the robustness of existing person search models.Code will be made publicly available.