We characterize the impact of single-time-step sensor attacks on Kalman filtering and fixed-point optimal smoothing for state estimation in linear time-invariant systems over a finite time window. We introduce a single noiseless, unattacked, full-state (perfect) measurement into the estimation process to reduce the attack's impact on the estimation process and increase the probability of detection under static χ2 tests. In the scalar case, we show that smoothing previous estimates with the perfect measurement to update detection statistics achieves higher attack detectability at the attack time than online Kalman filter-based statistics. Numerical experiments suggest that our findings generalize to multi-dimensional systems.