Recent Large Language Models (LLMs) have shown strong performance on automated program repair across standard benchmarks. However, these benchmarks evaluate models on a single canonical form of buggy code and do not reflect the syntactic variations commonly observed in real-world software, leaving robustness largely unexamined. In this work, we construct HEJ-Robust, a robustness benchmark built from HumanEval-Java-Bug using eight semantic-preserving code transformations, resulting in 1,450 transformed instances. We evaluate five fine-tuned LLMs on this benchmark and show that model performance drops by over 50% under several transformations, indicating that current LLM-based repair models lack robustness to minor syntactic variations.