Generating safe and reliable trajectories for autonomous vehicles in long-tail scenarios remains a significant challenge. High-lateral-acceleration maneuvers (e.g., sharp turns) represent a critical subset of these rare but high-risk situations. Existing trajectory planners, often trained on imbalanced datasets, struggle in these scenarios due to insufficient exposure to relevant data, leading to incomplete decision-making information at inference time, particularly concerning the precise interplay of vehicle dynamics, road geometry, and environmental constraints at the limits of handling. Consequently, these planners generate suboptimal or unsafe trajectory predictions in high-lateral-acceleration scenarios. To address this gap, we introduce ReflexDiffusion, a novel inference-stage framework that enhances diffusion-based trajectory planners through reflective adjustment. During iterative denoising, after each standard trajectory update step, we inject an additional adjustment: explicitly amplifying critical conditioning signals (e.g., road curvature, ego lateral dynamics) by computing the gradient between conditional and unconditional noise predictions. This forces the trajectory to strictly adhere to physical constraints, especially improving stability during high-lateral-acceleration maneuvers where precise vehicle-road interaction is paramount. Evaluated on nuPlan Test14-hard benchmark, our approach elevates the driving score for high-lateral-acceleration scenarios by 14.1% over the state-of-the-art. This demonstrates that our method dynamically reinforces safety-critical constraints at handling limits, effectively compensating for training data sparsity through inference-time trajectory optimization. Moreover, our approach is highly generalizable and can be deployed to other diffusion-based planners without modifying the model architecture.