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Detects adversarial attempts to infer whether specific records were used in an AI model's training set by analyzing raw prediction confidence scores and identifying suspicious query patterns such as near-duplicate submissions or systematic sweeps.
Detects anomalous AI model inference activity where a caller submits a high volume of near-duplicate queries while frequently requesting raw confidence scores. This behavior is indicative of a membership-inference privacy attack, where an adversary attempts to determine if specific data points were included in the model's training set by analyzing query response patterns.
Detects anomalous query patterns against an AI inference API, characteristic of a membership inference attack. The rule identifies callers submitting a high volume of near-duplicate or minimally-perturbed inputs while requesting raw confidence scores, suggesting an attempt to probe the model to determine if specific data points were used in its training set.