Preserving safety alignment during large language models fine-tuning is critical, however, recent studies have demonstrated that even benign fine-tuning data may contain safety-degrading samples that silently undermine such alignment. Existing approaches typically identify safety-degrading samples using representations from a single safety-sensitive layer. However, whether a single safety-sensitive layer is sufficient for identifying such samples in multilingual settings remains unclear, where representation patterns may vary across languages. Our cross-lingual analysis shows that sensitive layers are only partially shared across languages and that safety-relevant signals often span multiple layers. Motivated by these observations, we propose MMSafe, a multi-layer framework for multilingual safety-degrading data identification that jointly leverages shared and language-specific sensitive layers. Extensive experiments across multiple models, languages, and safety benchmarks demonstrate that MMSafe reduces the average harmful-response ratio by 60% compared with random filtering and outperforms the strongest single-layer baseline, demonstrating the effectiveness of multi-layer modeling for robust multilingual safety alignment. Code is available at https://anonymous.4open.science/r/MMSAFE.