This question requires a highly demanding conjunction of events: a frontier model must learn from deployment sessions into shared weights; behavior from one identifiable user must propagate to unrelated users; the effect must be measurable; and the provider must publicly acknowledge the single-session origin. As of mid-2026, the trajectory of commercial AI architecture strongly favors per-tenant adapters (like LoRA), which structurally prevent cross-user propagation and preserve enterprise privacy. We estimate a roughly 30% probability that the industry successfully transitions to shared-weight continuous learning loops, exposing models to these data-poisoning or cross-contamination vulnerabilities. Because this incident can only occur under that specific technological trajectory, the cumulative probability is capped at 30%, rigorously resolving the median and upper percentiles to 'never.' Within the aggressive scenario where such architectures are adopted, an acknowledged failure would necessarily follow the prerequisite implementations of rapid shared-weight updates (which reaches a 10th percentile probability by 2030) and cross-customer merging (10th percentile in 2031) . Accounting for the formidable barrier of a provider willingly admitting a severe, targeted vulnerability leakage in production, the 10th and 25th percentiles are pushed to 2032 and 2038, respectively.