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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 proper testing environments for corrupted conditions persists as a challenge, since extensive collection of new person images attempting to cover numerous corruption scenarios inevitably introduces privacy concerns. In this context, we establish an evaluation framework for analyzing corruption robustness using existing publicly available data, and introduce two new benchmarks: CUHK-SYSU-C and PRW-C. Based on this framework incorporating 18 corruptions with 5 severity levels each, we conduct an extensive robustness evaluation of popular person search methods. Moreover, we explore a straightforward solution that naturally arises: integrating corruption-robust detection and re-identification models. Our experiments reveal that existing person search models remain highly vulnerable to corruption, and this issue is not resolved by the simple integration approach. To analyze the underlying reasons, 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 analyses, we propose a foreground-aware augmentation and corresponding robust proposal regularizer 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.