Text-to-motion (T2M) generation has achieved impressive realism but poses significant safety risks by faithfully executing unsafe textual prompts. Existing motion unlearning methods face dual challenges: brittle keyword filtering and discrete codebook manipulation, which may degrade benign generation quality and introduce jerky transitions. To address these challenges, we propose SafeMo, a linguistically grounded framework that leverages Large Language Models (LLMs) to align motion generation with safety constraints. First, we introduce SafeMoEngine, an LLM-agent pipeline that autonomously classifies unsafe intents and performs semantic rewriting to construct SafeMoVAE-29K, the first safety-aligned text-to-motion dataset. Second, we propose Minimal Motion Unlearning (MMU), a continuous-space unlearning strategy that projects unsafe semantic concepts into a task vector for precise negation, avoiding the quantization artifacts of discrete methods. Experiments demonstrate SafeMo’s effective unlearning performance by more strongly suppressing unsafe text–motion retrieval than the prior SOTA motion unlearning baseline LCR, reducing forget-set R@1 by 35.3% and achieving 2.61× normalized retrieval trade-off score (NRTS), while showing better or competitive benign utility on safe prompts, with similar trends on Motion-X. Code and checkpoints are publicly available at https://github.com/AIGeeksGroup/SafeMo. Project page: https://aigeeksgroup.github.io/SafeMo.