MoE routing logs expose a new membership privacy risk
Logs of which experts a mixture-of-experts model uses can help attackers tell whether data appeared in its fine-tuning set, creating a privacy risk for teams that expose router telemetry. The attack combines normal output clues with aggregated routing features and trains a membership classifier using independently fine-tuned shadow models. Across three architectures and three domains, it raised true-positive rate at 1% false-positive rate by 2.7 to 9.4 percentage points, even when routers were frozen or telemetry was restricted.