Diffusion language models generate text by denoising a masked canvas, but usually require the canvas length to be fixed before decoding. This leads to an allocation dilemma: short canvases may truncate outputs, while overlong canvases waste computation and introduce noisy continuations. Existing variable-length methods adapt the canvas through expansion or length search, but largely ignore whether such intervention is safe. We argue that canvas editing can perturb partially stabilized denoising trajectories, and propose Safe Adaptive Canvas Decoding (SACD), a training-free framework for conservative canvas control. SACD diagnoses allocation, estimates intervention risk, and chooses among expand, shrink, and keep. Experiments across reasoning, code generation, and infilling tasks show that SACD improves accuracy, latency, and token utilization across multiple DLM backbones, highlighting safe sparse intervention as a key principle for variable-length diffusion decoding.