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Identifies an Amazon Bedrock API key phantom user (an IAM user whose name starts with "BedrockAPIKey-") acting as the caller of a non-Bedrock API request, such as IAM, STS, EC2, VPC, or KMS calls. These users are provisioned by AWS to back a Bedrock bearer token and carry the AmazonBedrockLimitedAccess managed policy, which also grants IAM, VPC, and KMS reconnaissance. A phantom user performing activity outside of Bedrock indicates its credentials are being used beyond their intended scope, which is the privilege-escalation path realized: an attacker who created standard IAM access keys for the phantom user is now using them for reconnaissance or lateral movement outside the Bedrock authentication boundary.
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Identifies an Amazon Bedrock API key (bearer token) being used to perform a destructive or anti-recovery control-plane action, such as deleting a guardrail, deleting a custom or imported model, removing provisioned throughput, or disabling model invocation logging. Bedrock API keys are bearer credentials intended for model invocation (InvokeModel, Converse); using one to delete Bedrock resources or disable logging is inconsistent with that purpose and is characteristic of LLMjacking or sabotage following key theft. Every Bedrock API key call is identifiable in CloudTrail by "additionalEventData.callWithBearerToken" being true. The rule matches regardless of outcome, because a destructive attempt via a bearer token is suspicious even when denied.
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Identifies standard IAM credentials being added to an Amazon Bedrock API key phantom user, whose user name starts with "BedrockAPIKey-": either a long-term access key (CreateAccessKey) or a console password / login profile (CreateLoginProfile, UpdateLoginProfile). When a long-term Bedrock API key is generated through the AWS Console, AWS silently provisions a "BedrockAPIKey-
" IAM user with the AmazonBedrockLimitedAccess managed policy. That user is intended only to back a Bedrock bearer token and should never hold standard programmatic keys or interactive console access. Adding either converts a Bedrock-scoped identity into general-purpose IAM credentials that inherit the policy's Bedrock control-plane and IAM, VPC, and KMS reconnaissance permissions and that persist after the Bedrock API key is revoked. This is the privilege-escalation and persistence pivot documented for Bedrock API key phantom users, and there is no legitimate workflow that produces it.
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Detects control-plane mutations to AWS Bedrock knowledge bases and their backing RAG data sources via CloudTrail. An adversary with access to Bedrock Agent APIs can poison the corpus that RAG-enabled models treat as authoritative by ingesting attacker-controlled documents (IngestKnowledgeBaseDocuments, StartIngestionJob), deleting legitimate documents (DeleteKnowledgeBaseDocuments), or repointing/altering the data source itself (CreateDataSource, UpdateDataSource, DeleteDataSource, UpdateKnowledgeBase). Because downstream applications and users trust model answers grounded in this stored data, tampering with the corpus is a stored data manipulation that can drive misinformation, fraud, or manipulated decisions at inference time. This is a New Terms rule that looks for the first time a given identity ARN performs one of these knowledge base or data source mutations within the history window.
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Identifies AWS Bedrock Agent creation performed directly by an IAM user or the root account. Bedrock Agents are autonomous AI systems that execute multi-step tasks, invoke Lambda action groups to call external APIs, and query knowledge bases. Adversaries with access to an AWS account can create rogue agents configured to exfiltrate data via action group Lambda functions, pivot to other services, or act as a persistent AI-driven command-and-control channel. This rule is scoped to IAMUser and Root identity types — AssumedRole sessions (which represent automated CI/CD pipelines and SSO-federated engineers) are excluded to avoid global false positives from legitimate deployment automation that varies widely across customer environments.
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Detects modification or deletion of resource-based access policies on AWS Bedrock resources via the PutResourcePolicy and DeleteResourcePolicy API calls. Resource-based policies govern which principals (including external accounts) may access Bedrock resources such as agents, knowledge bases, and custom models. An adversary may attach a resource policy granting an external or unexpected principal access to a Bedrock resource to establish persistence or enable cross-account access, or may delete an existing policy to weaken access controls. These changes should be validated for principal ownership and least-privilege intent.
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Detects failed, access-denied attempts to modify or delete resource-based access policies on AWS Bedrock resources via the PutResourcePolicy and DeleteResourcePolicy API calls. Resource-based policies govern which principals (including external accounts) may access Bedrock resources such as agents, knowledge bases, and custom models. A principal that is repeatedly denied when attempting to attach or remove these policies may be a compromised or under-privileged identity probing for the ability to grant external or cross-account access, or to weaken existing access controls. Unlike the companion rule that detects successful changes, this rule surfaces the attempt itself, which is a high-signal indicator of credential boundary-testing even though no change occurred.
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Detects deletion or modification of AWS Bedrock Automated Reasoning policies via the DeleteAutomatedReasoningPolicy, UpdateAutomatedReasoningPolicy, or UpdateAutomatedReasoningPolicyAnnotations CloudTrail actions. Automated Reasoning policies are a Bedrock safety and validation control that constrains model outputs against formal rules. An adversary who deletes a policy or alters the policy definition or its annotations weakens an enforced output-validation defense, potentially allowing unsafe or non-compliant model responses to pass unchecked. Benign build, test-workflow, and test-case CRUD operations are intentionally excluded as they have no coherent abuse path.
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Detects deletion, weakening, or version management of AWS Bedrock guardrails via the DeleteGuardrail, UpdateGuardrail, DeleteEnforcedGuardrailConfiguration, or PutEnforcedGuardrailConfiguration APIs. Bedrock guardrails enforce content, topic, word, and sensitive-information policies on model invocations. Deleting a guardrail, loosening its policies, removing or overwriting the organization-enforced guardrail configuration, or creating a new version to enforce a weakened configuration allows an adversary to bypass these protections — the cloud control-plane equivalent of disabling a security tool. This activity should be validated against approved change management and the responsible identity.
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Detects when an AWS Bedrock model invocation logging configuration is deleted or overwritten via the DeleteModelInvocationLoggingConfiguration or PutModelInvocationLoggingConfiguration API calls. Model invocation logging is the source that feeds the logs-aws_bedrock.invocation-* dataset relied upon by all data-plane Bedrock detections. An adversary who has gained access to a Bedrock environment can blind defenders by deleting this configuration, or by using the Put API to redirect logs to an attacker-controlled or non-monitored S3 bucket or CloudWatch log group. Because this single control-plane action can neutralize the entire data-plane detection stack, it is a high-value evasion technique that should be validated against expected administrative change activity.
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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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Identifies when access to an Amazon Bedrock foundation model is enabled at the account level, 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. Adversaries or a compromised principal may enable model access to abuse expensive models (LLMjacking), to establish a durable ability to invoke models within the account, or to bypass organizational controls. This activity is distinct from changes to a resource-based model invocation policy and is identified by the Bedrock control-plane API calls that grant model entitlements and agreements.
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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 modification of deployed Amazon Bedrock agents and their action groups, collaborators, or aliases via the Bedrock Agent control plane. Adversaries with access to an AWS account can tamper with an existing, trusted agent by altering its instructions (UpdateAgent), adding or changing action groups that wire the agent to Lambda functions or APIs (CreateAgentActionGroup, UpdateAgentActionGroup), attaching or modifying collaborators (AssociateAgentCollaborator, UpdateAgentCollaborator), or repointing an alias to a tampered version (CreateAgentAlias, UpdateAgentAlias). A PrepareAgent call is required to make a tampered configuration live. By implanting malicious behavior into an agent that legitimate users continue to invoke, an attacker can maintain durable access through a trusted component. Creation of brand-new agents (CreateAgent) is intentionally excluded as lower-signal activity.
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Detects when an AWS Bedrock custom model is imported or deployed, or when a marketplace model endpoint is created or registered, via the CreateModelImportJob, CreateCustomModelDeployment, CreateMarketplaceModelEndpoint, or RegisterMarketplaceModelEndpoint API calls. These actions introduce a model artifact from outside the organization's trusted training and approval pipeline. A backdoored, poisoned, or attacker-supplied model that downstream applications subsequently invoke represents a software supply-chain compromise. New model imports and marketplace endpoint registrations should be validated for artifact provenance (S3 source ownership), the registering identity, and whether the model originates from an approved internal pipeline.
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