Time-Series Foundation Models Anchor to Corrupted Context: A Diagnosis, a Blind Fix, and the Limits of Fixing

Time-series foundation models forecast from a window of recent history with no training or data audit in between, transferring responsibility for data quality to the model. Operational series carry sentinel values, filled gaps and transient level artifacts, and a defect near the forecast origin forces an implicit decision about whether to trust them. We study what such defects do to zero-shot forecasts, how much of the damage a blind test-time repair removes, what produces the sensitivity, and where repair meets a limit no detector can cross. We audit ordinary defects across foundation models from several organizations and architectures, reporting aggregates on normalized errors with equal dataset weights rather than raw pooling, which one dataset dominates. We then propose a blind, training-free sanitizer pairing robust clipping with a gated level correction whose onset is estimated rather than assumed, and localize the mechanism by intervening on where the level is perturbed. Because a transient artifact and a just-started regime change are observationally identical at the origin, no context-only policy is accurate in both worlds, and an equal-weight mixture of the two readings is minimax-optimal. Fragility concentrates in level structure and grows with model scale, the sensitivity is extrapolation of the recent level rather than normalization, and the mixture alone keeps its stated uncertainty in either world. Code is available at https://anonymous.4open.science/r/tsfm-iclr27-AEB1/.