We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce **AIMS**, a human-annotated dataset containing 1,724 intent-label annotations over 1,275 unique difficult safety prompts, each paired with an intent description and harm label. We use **AIMS** to evaluate intent-aware training across supervised fine-tuning, preference learning, reasoning distillation, and reinforcement learning. Despite its size, **AIMS** enables competitive safety classifiers across training regimes: DPO from model-generated intent errors improves over SFT, and intent-conditioned distillation outperforms reasoning-only distillation in most teacher–student pairs. Most notably, directly rewarding intent faithfulness with GRPO yields the strongest average performance across five external safety benchmarks, while our intent-aware models form the inference latency–F1 Pareto frontier. These results show that faithful intent modeling is a compact, high-quality supervision signal for more robust safety classifiers.