Detects a GCP Vertex AI GenerateContent exchange whose prompt or model reply contains a known credential pattern (AWS access keys, GitHub tokens, PEM private keys, and similar), or whose assistant reply warns about exposed keys and revocation. Secrets in prompt/response logs are visible to anyone with access to BigQuery prompt-response export and Elastic.

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Detects when Azure OpenAI requests result in zero response length, potentially indicating issues in output handling that might lead to security exploits such as data leaks or code execution. This can occur in cases where the API fails to handle outputs correctly under certain input conditions.

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Detects a Microsoft Foundry chat, sent through API Management, whose prompt or assistant reply contains a known credential pattern or an email address together with a password assignment. The model often refuses the request and Azure content filters stay clear, so the secret is only visible in the logged message text.

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Identifies prompts sent to an Amazon Bedrock AgentCore runtime that contain AWS access key identifiers (AKIA long-term or ASIA temporary/STS), Amazon Bedrock API keys (ABSK bearer tokens), or PEM-encoded private keys. The runtime application logs record the caller-supplied prompt; credentials embedded in a prompt are exposed to the model provider, persisted in observability logs, and may be returned in completions or used by downstream tools. This commonly indicates accidental secret leakage by a user or application, or an attempt to stage credentials for misuse through the agent. Secrets should never be passed to an agent in clear text.

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Identifies an Amazon Bedrock model invocation whose prompt or completion contains an AWS access key identifier (AKIA long-term or ASIA temporary/STS, followed by 16 characters), an Amazon Bedrock API key (ABSK bearer token), or a PEM private-key block. Credentials in the model input mean an application or user is sending secrets to the model, exposing them to invocation logging, the model provider, and prompt history; credentials in the model output mean the model is emitting secrets, which can result from training-data leakage, poisoned context, or a prompt-injection-driven exfiltration attempt. Either case is a credential-exposure event that warrants immediate rotation of the affected secret.

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Detects repeated compliance violation 'BLOCKED' actions coupled with specific policy name such as 'sensitive_information_policy', indicating persistent misuse or attempts to probe the model's denied topics.

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