Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems often suffer from poor cross-domain generalization, weak rationale grounding, and reliance on costly prompting-based large language models (LLMs). We introduce CHARM, a MA**C** and **H**ate-speech-**A**ware **R**ationale-aligned **M**oral foundation detection framework built on a lightweight fine-tuned LLM, which integrates complementary moral grounding, rationale alignment, and polarity-aware hate speech signals to support more robust and faithful moral prediction. Unlike prior dictionary-, fine-tune-, or prompt-based detectors, which decouple computation from psychological theory, CHARM is built so that each component — MAC cross-attention, rationale alignment, and hate-speech modulation — operationalises a distinct psychological construct. Using only 30% of the training data required by prior fine-tuned baselines, CHARM improves AUC by up to 15.3% in-domain, surpasses these baselines on every out-of-domain dataset in both AUC and F1, and offers a scalable, low-cost alternative to LLM-based detectors. We further apply CHARM to large-scale COVID-19 discourse on Twitter and show that moral value alignment is strongly associated with online endorsement behavior. By making moral framing measurable at scale, CHARM offers a practical tool for studying the spread of morally charged misinformation.