Activation steering offers a lightweight approach to LLM safety, yet existing methods relying on static, linear perturbations often struggle with complex boundaries, leading to severe safety-utility trade-offs such as over-refusal. To address this, we introduce WassSteer. First, we propose a Safety Routing Mechanism that achieves context-aware sparse control by selectively intervening only on unsafe activations. Second, we reformulate steering as a transport problem on the Wasserstein Representation Manifold. Utilizing Flow Matching, WassSteer learns a non-linear velocity field to transport harmful activations toward safe regions along Wasserstein Geodesics, effectively neutralizing toxicity while maximally preserving semantic content. Experiments across Llama-3, Gemma-2, and Qwen-2.5 demonstrate that WassSteer significantly outperforms baselines against diverse jailbreaks while maintaining high performance on general benchmarks, effectively reshaping the safety-utility Pareto frontier.