Detects a single source IP making a high number of Vertex AI GenerateContent (or StreamGenerateContent) audit calls against the same model resource in the lookback window. That pattern fits model extraction, automated scraping, or a compromised caller burning prediction quota.

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Detects a high number of Vertex AI prompt-response log rows and high total token usage for one model in the lookback window. Totals use provider usage_metadata.total_token_count (includes thinking tokens when billed). High request and token volume can indicate model extraction, quota abuse, or a compromised caller. Tune thresholds for your baseline.

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Detects patterns indicative of Denial-of-Service (DoS) attacks on machine learning (ML) models, focusing on unusually high volume and frequency of requests or patterns of requests that are known to cause performance degradation or service disruption, such as large input sizes or rapid API calls.

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Detects one caller IP sending a high number of requests and consuming a high number of tokens against the same Foundry deployment and operation during the rule lookback. That rate fits a script or a stolen key burning quota. The rule uses RequestResponse logs and does not need the prompt text. Tune the request count and token sum in the query.

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Detects creation, modification, or deletion of AWS Bedrock Provisioned Model Throughput via the CreateProvisionedModelThroughput, UpdateProvisionedModelThroughput, and DeleteProvisionedModelThroughput APIs. Provisioned Throughput reserves dedicated, billed model capacity for Amazon Bedrock. An adversary who scales this capacity up can drive large, unauthorized cost (cloud resource/bill hijacking), while deleting reserved throughput can cause denial of service to production workloads that depend on that committed capacity. These control-plane changes should be validated against approved capacity-planning and change-management processes.

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Detects potential resource exhaustion or data breach attempts by monitoring for users who consistently generate high input token counts, submit numerous requests, and receive large responses. This behavior could indicate an attempt to overload the system or extract an unusually large amount of data, possibly revealing sensitive information or causing service disruptions.

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