The authors address the brittleness of test-time adaptation (TTA) under adversarially contaminated streams by proposing SAFER, a training-free framework for robust TTA. SAFER acts as an augmentation wrapper that replaces single-view predictions with a reliability-guided pooled predictor to stabilize online updates. For each test sample, the method generates stochastic augmentations and aggregates their outputs using correlation-weighted pooling combined with outlier detection. An adaptive-mixing extension is also introduced, which adjusts the weighting between original and augmented inputs based on feature disagreement signals to preserve clean performance. The researchers evaluated SAFER on PACS, VLCS, and OfficeHome benchmarks under PGD attacks at various rates. Results indicate that SAFER improves the resilience of TTA methods against adversarial attacks while maintaining competitive accuracy on clean data.