Detects Vertex AI authenticated caller activity for the same client.user.email from at least three countries and three source IPs within a four-hour interval. That pattern can indicate stolen user or service-account credentials reused from multiple regions, including VPN or cloud egress and return trips where first and last locations are close.

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Detects the same Vertex AI authenticated caller (client.user.email) generating aiplatform audit activity from geographically distant source IPs within a short window at an impossible travel speed. That pattern can indicate a stolen user or service-account credential used from multiple regions. Related: GCP Vertex AI Caller Activity From High Number of Countries covers slower multi-country reuse without a high-speed hop.

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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 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 an AWS principal using long-term IAM user credentials (AKIA* access key) enumerates available Bedrock foundation models and then invokes a model within the same 15-minute window. Most legitimate Bedrock workloads run under IAM roles with short-lived credentials; the combination of model enumeration followed by direct model invocation from a long-term IAM user key is unusual in production environments and consistent with an adversary using stolen credentials to discover and exploit available AI model capabilities. This pattern is associated with LLMjacking attacks where threat actors abuse compromised cloud credentials to run high-volume or high-cost model inference at the account owner's expense.

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Identifies failed, access-denied attempts to enable account-level access to an Amazon Bedrock foundation model, either by granting a foundation-model entitlement, submitting a use case for model access, or creating a foundation-model agreement (accepting the EULA). These account-level "model access" actions unlock a foundation model so that it can subsequently be invoked. A principal that is repeatedly denied when attempting these actions may be a compromised or under-privileged identity probing for the ability to unlock expensive models (LLMjacking) or to establish a durable ability to invoke models. Unlike the companion rule that detects successful model-access grants, this rule surfaces the attempt itself, which is a high-signal indicator of credential boundary-testing even though access was not granted.

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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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Monitors for suspicious activities that may indicate theft or unauthorized duplication of machine learning (ML) models, such as unauthorized API calls, atypical access patterns, or large data transfers that are unusual during model interactions.

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Identifies an Amazon Bedrock AgentCore execution role (an AssumedRole identity whose role name begins with "AgentCore-" or contains "BedrockAgentCore") making an AWS API call to a service it has not previously called. AgentCore runtimes normally interact only with Bedrock inference, AgentCore data-plane, and observability services (CloudWatch Logs, X-Ray, CloudWatch metrics), so an execution role suddenly calling STS, EC2, IAM, Secrets Manager, or other services is a strong indicator that the role's temporary credentials were exfiltrated from the agent's microVM (for example, via the Code Interpreter instance-metadata-service credential theft) and are being used outside the runtime for reconnaissance, privilege escalation, or lateral movement. Because the stolen credentials are recorded in CloudTrail under the execution role's own identity, the anomalous service usage, not the identity, is the detectable signal.

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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 prompts sent to an Amazon Bedrock AgentCore runtime that attempt to harvest credentials or coerce the agent into exfiltrating data. The runtime application logs capture the caller-supplied prompt; this rule flags prompts that reference the cloud instance metadata service (169.254.169.254, the ECS task metadata address, or the "latest/meta-data" / "security-credentials" paths), prompts that name AWS access or secret keys directly, and prompt-injection or jailbreak language ("ignore previous instructions", "developer mode", "do anything now") combined with intent to reveal secrets, system prompts, or send data to an external endpoint. Asking an agent to read instance metadata credentials or to exfiltrate secrets is rarely legitimate and indicates an attempt to weaponize the agent for credential theft, even when the model refuses the request.

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Identifies an AWS principal performing a high volume of Amazon Bedrock inference API calls against a single model within a short window. Membership inference attacks require hundreds to thousands of statistically similar queries whose prompts and responses are intentionally content-benign, making guardrail- and content-based rules ineffective. This rule detects the high-frequency single-model probing pattern that precedes membership inference and related exfiltration via the inference API. It is a behavioral / volumetric precursor: it does not observe model confidence scores and a fixed call-count threshold only catches the loud variant, so paced, low-and-slow, or credential-distributed probing will evade it. Definitive membership inference detection requires ML anomaly analysis over per-entity inference-rate and response-distribution baselines.

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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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