Visual artifacts remain a persistent challenge in diffusion models, even with training on massive datasets. Current solutions primarily rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place. We discover that a diffusion generative process undergoes three temporal phases: Profiling, Mutation, and Refinement. We observe that artifacts typically emerge during the Mutation phase, where certain regions exhibit anomalous score dynamics over time, causing abrupt disruptions in the normal evolution pattern. This, reveals why existing methods that quantify the spatial uncertainty of the final output, and therefore lack modeling of the temporal dynamics of the diffusion process, have shown to be inadequate for artifact localization. Based on these insights, we propose a novel method, ASCED (Abnormal Score Correction for Enhancing Diffusion), that detects artifacts by monitoring abnormal score dynamics during the diffusion process, and a trajectory-aware on-the-fly mitigation strategy that appropriate generation of noise in the detected areas. Unlike most existing methods that apply post hoc corrections, e.g., by applying a noising-denoising scheme after generation, our mitigation strategy does not involve additional diffusion steps.Extensive experiments demonstrate that our proposed approach effectively reduces artifacts across diverse domains, matching or surpassing existing supervised methods without additional training.