Detects the three-event chain produced by tools like aws_consoler that convert exfiltrated long-term IAM access keys into browser-accessible AWS console sessions: GetFederationToken obtains temporary credentials, GetSigninToken exchanges them for a federation sign-in token via the AWS federation endpoint, and ConsoleLogin confirms the resulting console session was opened — all from the same source IP within two minutes. This sequence is a high-confidence indicator of credential abuse using stolen IAM access keys.

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Detects the deletion of an Amazon GuardDuty threat intelligence set. Threat intelligence sets are custom lists of known-malicious IP addresses or domains that GuardDuty uses to generate findings when monitored resources communicate with those indicators. Deleting a threat intel set degrades GuardDuty's detection capability for known adversary infrastructure, allowing communication with threat-actor-controlled IP ranges to go undetected.

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Detects the creation of an Amazon EKS access entry followed by its deletion by the same identity within a short time window. EKS access entries define Kubernetes RBAC-level permissions for IAM principals in an EKS cluster. An adversary with EKS administrative access may temporarily grant themselves cluster access, use those permissions to create Kubernetes RBAC resources (ClusterRoleBindings, ServiceAccounts with privileged roles), and then delete the access entry to hide the evidence of the initial grant while retaining access through the Kubernetes-level backdoor.

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Detects the deletion of an Amazon GuardDuty publishing destination. Publishing destinations export GuardDuty findings to S3, Security Lake, or EventBridge for long-term retention and SIEM ingestion. An adversary with GuardDuty administrative access may delete a publishing destination to prevent findings from reaching external storage or a security operations center, reducing the visibility of their activity while leaving the GuardDuty detector active.

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Detects the deletion of an Amazon Detective behavior graph via the DeleteGraph API. Amazon Detective automatically collects log data from AWS services and uses machine learning, statistical analysis, and graph theory to build an interactive model of resource behaviors and interactions. Deleting a behavior graph destroys its historical analysis data and removes the ability to investigate security incidents using Detective's relationship mapping. An attacker with sufficient IAM permissions may delete the Detective graph to impair forensic investigation of a compromise.

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Identifies the first time, within a lookback window, an identity performs AWS Organizations or IAM account enumeration APIs. Attackers with compromised credentials often map the organization (accounts, OUs, roots, delegated admins) and account-level metadata (aliases, summary) using the AWS CLI or SDKs. This is a New Terms rule detecting a rare occurrence of the cloud.account.id and user.name pair for these actions.

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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 updates to an existing CloudTrail trail via UpdateTrail API which may reduce visibility, change destinations, or weaken integrity (e.g., removing global events, moving the S3 destination, or disabling validation). Adversaries can modify trails to evade detection while maintaining a semblance of logging. Validate any configuration change against approved baselines.

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Detects the deletion of one or more Amazon CloudWatch alarms using the "DeleteAlarms" API. CloudWatch alarms are critical for monitoring metrics and triggering alerts when thresholds are exceeded. An adversary may delete alarms to impair visibility, silence alerts, and evade detection following malicious activity. This behavior may occur during post-exploitation or cleanup phases to remove traces of compromise or disable automated responses.

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Detects the deletion of an Amazon CloudWatch Log Group using the "DeleteLogGroup" API. CloudWatch log groups store operational and security logs for AWS services and custom applications. Deleting a log group permanently removes all associated log streams and historical log data, which can eliminate forensic evidence and disrupt security monitoring pipelines. Adversaries may delete log groups to conceal malicious activity, disable log forwarding, or impede incident response.

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Detects the deletion of an Amazon CloudWatch log stream using the "DeleteLogStream" API. Deleting a log stream permanently removes its associated log events and may disrupt security visibility, break audit trails, or suppress forensic evidence. Adversaries may delete log streams to conceal malicious actions, impair monitoring pipelines, or remove artifacts generated during post-exploitation activity.

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Identifies attempts to delete AWS Config resources. AWS Config provides continuous visibility into resource configuration changes and compliance posture across an account. Deleting Config components can significantly reduce security visibility and auditability. Adversaries may delete or disable Config resources to evade detection, hide prior activity, or weaken governance controls before or after other malicious actions.

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Identifies an AWS Amazon Machine Image (AMI) being shared with another AWS account. Adversaries with access may share an AMI with an external AWS account as a means of data exfiltration. AMIs can contain secrets, bash histories, code artifacts, and other sensitive data that adversaries may abuse if shared with unauthorized accounts. AMIs can be made publicly available accidentally as well.

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Identifies the removal of access permissions from a shared AWS EC2 EBS snapshot. EBS snapshots are essential for data retention and disaster recovery. Adversaries may revoke or modify snapshot permissions to prevent legitimate users from accessing backups, thereby obstructing recovery efforts after data loss or destructive actions. This tactic can also be used to evade detection or maintain exclusive access to critical backups, ultimately increasing the impact of an attack and complicating incident response.

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Detects when Amazon Elastic Block Store (EBS) encryption by default is disabled in an AWS region. EBS encryption ensures that newly created volumes and snapshots are automatically protected with AWS Key Management Service (KMS) keys. Disabling this setting introduces significant risk as all future volumes created in that region will be unencrypted by default, potentially exposing sensitive data at rest. Adversaries may disable encryption to weaken data protection before exfiltrating or tampering with EBS volumes or snapshots. This may be a step in preparation for data theft or ransomware-style attacks that depend on unencrypted volumes.

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Identifies the creation of an AWS EC2 network access control list (ACL) or an entry in a network ACL with a specified rule number. Adversaries may exploit ACLs to establish persistence or exfiltrate data by creating permissive rules.

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Identifies the deletion of an Amazon Elastic Compute Cloud (EC2) network access control list (ACL) or one of its ingress/egress entries.

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Identifies when an EC2 Route Table has been created. Route tables can be used by attackers to disrupt network traffic, reroute communications, or maintain persistence in a compromised environment. This is a New Terms rule that detects the first instance of this behavior by a user or role.

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Identifies a change to an AWS Security Group Configuration. A security group is like a virtual firewall, and modifying configurations may allow unauthorized access. Threat actors may abuse this to establish persistence, exfiltrate data, or pivot in an AWS environment.

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Identifies discovery request DescribeInstanceAttribute with the attribute userData and instanceId in AWS CloudTrail logs. This may indicate an attempt to retrieve user data from an EC2 instance. Adversaries may use this information to gather sensitive data from the instance such as hardcoded credentials or to identify potential vulnerabilities. This is a New Terms rule that identifies the first time an IAM user or role requests the user data for a specific EC2 instance.

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Identifies the deletion of an Amazon EFS file system using the "DeleteFileSystem" API operation. Deleting an EFS file system permanently removes all stored data and cannot be reversed. This action is rare in most environments and typically limited to controlled teardown workflows. Adversaries with sufficient permissions may delete a file system to destroy evidence, disrupt workloads, or impede recovery efforts.

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Identifies when an Amazon EventBridge rule is disabled or deleted. EventBridge rules are commonly used to automate operational workflows and security-relevant routing (for example, forwarding events to Lambda, SNS/SQS, or security tooling). Disabling or deleting a rule can break critical integrations, suppress detections, and reduce visibility. Adversaries may intentionally impair EventBridge rules to disrupt monitoring, delay response, or hide follow-on actions.

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Detects the deletion of an Amazon GuardDuty detector. GuardDuty provides continuous monitoring for malicious or unauthorized activity across AWS accounts. Deleting the detector disables this visibility, stopping all threat detection and removing existing findings. Adversaries may delete GuardDuty detectors to impair security monitoring and evade detection during or after an intrusion. This rule identifies successful "DeleteDetector" API calls and can indicate a deliberate defense evasion attempt.

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Identifies AWS CloudTrail events where an IAM role's trust policy has been updated by an IAM user or Assumed Role identity. The trust policy is a JSON document that defines which principals are allowed to assume the role. An attacker may attempt to modify this policy to gain the privileges of the role. This is a New Terms rule, which means it will only trigger once for each unique combination of the "cloud.account.id", "user.name" and "entity.target.id" fields, that have not been seen making this API request.

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Identifies successful IAM API calls that create a new customer managed policy version or set the default version for an existing customer managed policy. Attackers with iam:CreatePolicyVersion or iam:SetDefaultPolicyVersion on a privileged policy can introduce a permissive policy document and activate it, escalating effective permissions without attaching a new policy. These APIs are high impact when the target policy is attached to powerful roles or users.

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Detects when an AWS Identity and Access Management (IAM) customer-managed policy is attached to a role by an unusual or unauthorized user. Customer-managed policies are policies created and controlled within an AWS account, granting specific permissions to roles or users when attached. This rule identifies potential privilege escalation by flagging cases where a customer-managed policy is attached to a role by an unexpected actor, which could signal unauthorized access or misuse. Attackers may attach policies to roles to expand permissions and elevate their privileges within the AWS environment. This is a New Terms rule that uses the "cloud.account.id", "user.name" and "entity.target.id" fields to check if the combination of the actor identity and target role name has not been seen before.

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Detects when an uncommon user or role creates an OpenID Connect (OIDC) Identity Provider in AWS IAM. OIDC providers enable web identity federation, allowing users authenticated by external identity providers (such as Google, GitHub, or custom OIDC-compliant providers) to assume IAM roles and access AWS resources. Adversaries who have gained administrative access may create rogue OIDC providers to establish persistent, federated access that survives credential rotation. This technique allows attackers to assume roles using tokens from an IdP they control. While OIDC provider creation is benign in some environments, it should still be validated against authorized infrastructure changes.

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An adversary with access to a set of compromised credentials may attempt to persist or escalate privileges by creating a new set of credentials for an existing user. This rule looks for use of the IAM CreateAccessKey API operation to create new programmatic access keys for another IAM user.

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Identifies attempts to disable or schedule the deletion of an AWS customer managed KMS Key. Disabling or scheduling a KMS key for deletion removes the ability to decrypt data encrypted under that key and can permanently destroy access to critical resources. Adversaries may use these operations to cause irreversible data loss, disrupt business operations, impede incident response, or hide evidence of prior activity. Because KMS keys often protect sensitive or regulated data, any modification to their lifecycle should be considered highly sensitive and investigated promptly.

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Identifies successful PutKeyPolicy calls on AWS KMS keys. The key policy is a resource-based policy that controls which principals can use the key for cryptographic operations and administration. Adversaries with "kms:PutKeyPolicy" may add or broaden principals (including external accounts) to decrypt or exfiltrate data protected by the key, or to preserve access after other credentials are rotated. This is distinct from disabling or scheduling deletion of the key.

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Identifies the deletion of an AWS Lambda function. Deleting a function removes its code, configuration, versions, and aliases. Adversaries may delete functions to disrupt business operations and automated workflows, to destroy attacker-deployed backdoors and remove evidence after achieving their objective, or to inhibit incident response. Because function deletion is destructive and often irreversible without redeployment, deletions performed by unexpected principals or outside change windows should be reviewed.

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Identifies when an AWS Lambda function policy is updated to allow public invocation. This rule detects use of the AddPermission API where the Principal is set to "*", enabling any AWS account to invoke the function. Adversaries may abuse this configuration to establish persistence, create a covert execution path, or operate a function as an unauthenticated backdoor. Public invocation is rarely required outside very specific workloads and should be considered high-risk when performed unexpectedly.

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Identifies the creation or update of an AWS Lambda function URL configured with an authentication type of NONE, which exposes the function to unauthenticated invocation directly from the public internet. Adversaries can use a public function URL to establish a durable, internet-reachable entry point for command and control, data egress, or on-demand execution of attacker-controlled code, bypassing the need for valid AWS credentials to invoke the function. Function URLs with public access should be rare and deliberate, so this configuration warrants review.

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Identifies when a Lambda layer is added to an existing AWS Lambda function. Lambda layers allow shared code, dependencies, or runtime modifications to be injected into a function’s execution environment. Adversaries with the ability to update function configurations may add a malicious layer to establish persistence, run unauthorized code, or intercept data handled by the function. This activity should be reviewed to ensure the modification is expected and authorized.

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Identifies the creation or modification of an Amazon RDS DB instance or cluster where the "publiclyAccessible" attribute is set to "true". Publicly accessible RDS instances expose a network endpoint on the public internet, which may allow unauthorized access if combined with overly permissive security groups, weak authentication, or misconfigured IAM policies. Adversaries may enable public access on an existing instance, or create a new publicly accessible instance, to establish persistence, move data outside of controlled network boundaries, or bypass internal access controls.

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Identifies the deletion of an Amazon RDS DB instance, Aurora cluster, or global database cluster. Deleting these resources permanently destroys stored data and can cause major service disruption. Adversaries with sufficient permissions may delete RDS resources to impede recovery, destroy evidence, or inflict operational impact on the environment.

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Identifies the modification of an AWS RDS DB instance or cluster to disable the deletionProtection feature. Deletion protection prevents accidental or unauthorized deletion of RDS resources. Adversaries with sufficient permissions may disable this protection as a precursor to destructive actions, including the deletion of databases containing sensitive or business-critical data. This rule alerts when deletionProtection is explicitly set to false on an RDS DB instance or cluster.

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Identifies when an AWS Route 53 private hosted zone is associated with a new Virtual Private Cloud (VPC). Private hosted zones restrict DNS resolution to specific VPCs, and associating additional VPCs expands the scope of what networks can resolve internal DNS records. Adversaries with sufficient permissions may associate unauthorized VPCs to intercept, observe, or reroute internal traffic, establish persistence, or expand their visibility within an AWS environment.

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Identifies the deletion of critical Amazon S3 bucket configurations such as bucket policies, lifecycle configurations or encryption settings. These actions are typically administrative but may also represent adversarial attempts to remove security controls, disable data retention mechanisms, or conceal evidence of malicious activity. Adversaries who gain access to AWS credentials may delete logging, lifecycle, or policy configurations to disrupt forensic visibility and inhibit recovery. For example, deleting a bucket policy can open a bucket to public access or remove protective access restrictions, while deleting lifecycle rules can prevent object archival or automatic backups. Such actions often precede data exfiltration or destructive operations and should be reviewed in context with related S3 or IAM events.

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Identifies the addition of an expiration lifecycle configuration to an Amazon S3 bucket. S3 lifecycle rules can automatically delete or transition objects after a defined period. Adversaries can abuse them by configuring auto-deletion of logs, forensic evidence, or sensitive objects to cover their tracks. This rule detects the use of the PutBucketLifecycle or PutBucketLifecycleConfiguration APIs with Expiration parameters, which may indicate an attempt to automate the removal of data to hinder investigation or maintain operational secrecy after malicious activity.

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Detects when an Amazon S3 bucket policy is modified to share access with an external AWS account. This rule analyzes PutBucketPolicy events and compares the S3 bucket’s account ID to any account IDs referenced in the policy’s Effect=Allow statements. If the policy includes principals from accounts other than the bucket owner’s, the rule triggers an alert. This behavior may indicate an adversary backdooring a bucket for data exfiltration or cross-account persistence. For example, an attacker who compromises credentials could attach a policy allowing access from an external AWS account they control, enabling continued access even after credentials are rotated. Note: This rule will not alert if the account ID is part of the bucket’s name or appears in the resource ARN. Such cases are common in standardized naming conventions (e.g., “mybucket-123456789012”). To ensure full coverage, use complementary rules to monitor for suspicious PutBucketPolicy API requests targeting buckets with account IDs embedded in their names or resources.

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Identifies the creation or modification of an S3 bucket replication configuration that sends data to a bucket in a different AWS account. Cross-account replication can be used legitimately for backup, disaster recovery, and multi-account architectures, but adversaries with write access to an S3 bucket may abuse replication rules to silently exfiltrate large volumes of data to attacker-controlled accounts. This rule detects "PutBucketReplication" events where the configured destination account differs from the source bucket's account, indicating potential unauthorized cross-account data movement.

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Identifies when server access logging is disabled for an Amazon S3 bucket. Server access logs provide a detailed record of requests made to an S3 bucket. When server access logging is disabled for a bucket, it could indicate an adversary's attempt to impair defenses by disabling logs that contain evidence of malicious activity.

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Identifies when object versioning is suspended for an Amazon S3 bucket. Object versioning allows for multiple versions of an object to exist in the same bucket. This allows for easy recovery of deleted or overwritten objects. When object versioning is suspended for a bucket, it could indicate an adversary's attempt to inhibit system recovery following malicious activity. Additionally, when versioning is suspended, buckets can then be deleted.

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Identifies when a user subscribes to an SNS topic using a new protocol type (ie. email, http, lambda, etc.). SNS allows users to subscribe to recieve topic messages across a broad range of protocols like email, sms, lambda functions, http endpoints, and applications. Adversaries may subscribe to an SNS topic to collect sensitive information or exfiltrate data via an external email address, cross-account AWS service or other means. This rule identifies a new protocol subscription method for a particular user.

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Detects the rare occurrence of a user or role accessing AWS Systems Manager (SSM) inventory APIs or running the AWS-GatherSoftwareInventory job. These APIs reveal detailed information about managed EC2 instances including installed software, patch compliance status, and command execution history. Adversaries may use these calls to collect software inventory while blending in with legitimate AWS operations. This is a New Terms rule that detects when a user accesses these reconnaissance APIs for the first time.

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An adversary with access to a set of compromised credentials may attempt to verify that the credentials are valid and determine what account they are using. This rule looks for the first time an identity has called the STS GetCallerIdentity API, which may be an indicator of compromised credentials. A legitimate user would not need to perform this operation as they should know the account they are using.

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Identifies successful AWS API calls where the CloudTrail user agent indicates offensive tooling or automated credential verification. This includes the AWS CLI or Boto3 reporting a Kali Linux distribution fingerprint (distrib#kali), and clients that identify as TruffleHog, which is commonly used to validate leaked secrets against live AWS APIs. These patterns are uncommon for routine production workloads and may indicate compromised credentials, unauthorized access, or security tooling operating outside approved scope.

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Detects the first occurrence of a user identity accessing AWS Systems Manager (SSM) SecureString parameters using the GetParameter or GetParameters API actions with credentials in the request parameters. This could indicate that the user is accessing sensitive information. This rule detects when a user accesses a SecureString parameter with the withDecryption parameter set to true. This is a New Terms rule that detects the first occurrence of an AWS identity accessing SecureString parameters with decryption.

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Identifies the deletion of one or more flow logs in AWS Elastic Compute Cloud (EC2). An adversary may delete flow logs in an attempt to evade defenses.

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Identifies the deletion of an AWS Web Application Firewall (WAF) Web ACL. Web ACLs are the core enforcement objects in AWS WAF, defining which traffic is inspected, allowed, or blocked for protected applications. Deleting a Web ACL removes all associated rules, protections, and logging configurations. Adversaries who obtain sufficient privileges may delete a Web ACL to disable critical security controls, evade detection, or prepare for downstream attacks such as web-application compromise, data theft, or resource abuse. Because Web ACLs are rarely deleted outside of controlled maintenance or infrastructure updates, unexpected deletions may indicate potential defense evasion.

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Identifies the deletion of an AWS Web Application Firewall (WAF) rule or rule group. WAF rules and rule groups enforce critical protections for web applications by filtering malicious HTTP requests, blocking known attack patterns, and enforcing access controls. Deleting these rules—even briefly—can expose applications to SQL injection, cross-site scripting, credential-stuffing bots, or targeted exploitation. Adversaries who have gained sufficient permissions may remove WAF protections as part of a broader defense evasion or impact strategy, often preceding data theft or direct application compromise.

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This rule detects the first time a principal calls AWS CloudFormation CreateStack, CreateStackSet or CreateStackInstances API. CloudFormation is used to create a collection of cloud resources called a stack, via a defined template file. An attacker with the appropriate privileges could leverage CloudFormation to create specific resources needed to further exploit the environment. This is a new terms rule that looks for the first instance of this behavior for a role or IAM user within a particular account.

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An adversary with access to a compromised AWS service such as an EC2 instance, Lambda function, or other service may attempt to leverage the compromised service to access secrets in AWS Secrets Manager. This rule looks for the first time a specific user identity has programmatically retrieved a secret value from Secrets Manager using the GetSecretValue action. This rule assumes that AWS services such as Lambda functions and EC2 instances are setup with IAM role's assigned that have the necessary permissions to access the secrets in Secrets Manager. An adversary with access to a compromised AWS service would rely on its' attached role to access the secrets in Secrets Manager.

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Identifies when a specified inbound (ingress) rule is added or adjusted for a VPC security group in AWS EC2. This rule detects when a security group rule is added that allows traffic from any IP address or from a specific IP address to common remote access ports, such as 22 (SSH) or 3389 (RDP). Adversaries may add these rules to allow remote access to VPC instances from any location, increasing the attack surface and potentially exposing the instances to unauthorized access.

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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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This rule identifies potentially suspicious activity by detecting instances where a single IAM user's temporary session token is accessed from multiple IP addresses within a short time frame. Such behavior may suggest that an adversary has compromised temporary credentials and is utilizing them from various locations. To enhance detection accuracy and minimize false positives, the rule incorporates criteria that evaluate unique IP addresses, user agents, cities, and networks. These additional checks help distinguish between legitimate distributed access patterns and potential credential misuse. Detected activities are classified into different types based on the combination of unique indicators, with each classification assigned a fidelity score reflecting the likelihood of malicious behavior. High fidelity scores are given to patterns most indicative of threats, such as multiple unique IPs, networks, cities, and user agents. Medium and low fidelity scores correspond to less severe patterns, enabling security teams to effectively prioritize alerts.

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Detects when a single AWS resource is running multiple read-only, discovery API calls in a 10-second window. This behavior could indicate an actor attempting to discover the AWS infrastructure using compromised credentials or a compromised instance. Adversaries may use this information to identify potential targets for further exploitation or to gain a better understanding of the target's infrastructure.

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Detects sensitive AWS IAM API operations executed using temporary session credentials (access key IDs beginning with "ASIA"). Temporary credentials are commonly issued through sts:GetSessionToken, sts:AssumeRole, or AWS SSO logins and are meant for short-term use. It is unusual for legitimate users or automated processes to perform privileged IAM actions (e.g., creating users, updating policies, or enabling/disabling MFA) with session tokens. This behavior may indicate credential theft, session hijacking, or the abuse of a privileged role’s temporary credentials.

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Correlates open detection alerts that share the same long-term IAM access key ID ( prefix AKIA). It fires when the rule AWS Long-Term Access Key First Seen from Source IP (rule_id: 9f8e3c5e-f72e-4e91-93f6-e98a4fae3e4f) has triggered for that key and at least one other open alert for the same key is medium, high, or critical severity. This higher-order rule helps prioritize long-term key novelty when it co-occurs with elevated detections that may indicate post-compromise activity.

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Detects attempts to create or enable a Virtual MFA device (CreateVirtualMFADevice, EnableMFADevice) using temporary AWS credentials (access keys beginning with ASIA). Session credentials are short-lived and tied to existing authenticated sessions, so using them to register or enable MFA devices is unusual. Adversaries who compromise temporary credentials may abuse this behavior to establish persistence by attaching new MFA devices to maintain access to high-privilege accounts despite key rotation or password resets.

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Identifies when the same AWS principal, from the same source IP, successfully invokes read-only S3 control-plane APIs that reveal bucket posture across many buckets in a short period. This pattern can indicate automated reconnaissance or security scanning, similar to CSPM tools and post-compromise enumeration. The rule excludes AWS service principals, requires programmatic-style sessions (not Management Console credentials), and requires populated resource and identity fields so nulls do not skew cardinality.

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Flags the first time a given IAM principal invokes a narrow set of high-signal discovery APIs (credential check, account and IAM enumeration, bucket and compute inventory, logging introspection) from a source IP whose autonomous system number (ASN) matches a curated set commonly associated with consumer VPN brands, VPN-heavy hosting, and provider networks referenced in public reporting on TeamPCP activity (for example 31173 Services AB AS39351 and Oy Crea Nova Hosting Solution Ltd). Broad List*/Describe* patterns are intentionally omitted to reduce noise. Hosting ASNs are heavily dual-use; validate source.as.number in your data and extend event.action only when your baseline allows it.

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Detects CloudTrail PutEventSelectors calls where the legacy event selectors explicitly set includeManagementEvents to false, disabling capture of all management API calls for that trail. Unlike StopLogging or DeleteTrail — which leave an obvious trace of the trail being stopped or removed entirely — this technique leaves the trail appearing active and healthy in the console while silently blinding defenders to subsequent IAM changes, credential operations, and resource abuse. This technique is documented in Stratus Red Team as aws.defense-evasion.cloudtrail-event-selectors and is a known pre-exfiltration step.

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Detects any attempt, successful or denied, for a member account to leave an AWS Organization via the LeaveOrganization API. Leaving an organization immediately strips the account of every Service Control Policy (SCP) guardrail the organization enforces, removes it from centralized CloudTrail aggregation, and eliminates the management account's ability to audit or control it going forward. An adversary who has gained root or organization-management-capable access in a member account may use this technique to escape organizational security controls and operate unmonitored. Denied attempts are included because a blocked call is just as strong a signal of intent as a successful one, and is often the only trace left when the account's default permissions correctly prevent the action.

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Detects the closure of an AWS account via the CloseAccount API. This can be called either by the account itself (account.amazonaws.com, self-service closure) or by an AWS Organizations management account against one of its member accounts (organizations.amazonaws.com). Account closure triggers a 90-day grace period during which the account is suspended before permanent termination, and is one of the most destructive and disruptive actions available in AWS. It removes access to all resources and data in the account for the duration of the suspension. An adversary with root-level access in a member account, or management-level access to an organization, may close accounts to destroy evidence, disrupt business operations, or eliminate compute and data resources. A malicious insider could use the same action for sabotage.

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Identifies a principal that, within a short window, both registers an Amazon ECS task definition using a public / non-ECR container image at a high CPU allocation (8 or 16 vCPU) AND launches ECS workloads (RunTask, StartTask, or CreateService). Registering a public miner image at maximum compute and then launching it is the ECS/Fargate cryptocurrency-mining deployment pattern seen after credential compromise. Requiring both the mining-signature registration and a launch by the same principal confirms an actual deployment rather than a standalone (possibly benign) task-definition registration, which sharply reduces false positives from high-compute workloads that are merely registered.

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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 the creation of a new AWS CloudShell environment. CloudShell is a browser-based shell that provides command-line access to AWS resources directly from the AWS Management Console. The CreateEnvironment API is called when a user launches CloudShell for the first time or when accessing CloudShell in a new AWS region. Adversaries with console access may use CloudShell to execute commands, install tools, or interact with AWS services without needing local CLI credentials. Monitoring environment creation helps detect unauthorized CloudShell usage from compromised console sessions.

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Detects Cloudtrail logging suspension via StopLogging API. Stopping CloudTrail eliminates forward audit visibility and is a classic defense evasion step before sensitive changes or data theft. Investigate immediately and determine what occurred during the logging gap.

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Detects successful Amazon EKS UpdateClusterConfig requests that disable control plane logging. Disabling EKS API server and control plane logs can reduce visibility into cluster activity and may indicate defense evasion following compromised AWS credentials or unauthorized administrative access. EKS control plane logging changes are typically rare and should align with approved maintenance or cost optimization workflows.

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Detects the deactivation of a Multi-Factor Authentication (MFA) device in AWS Identity and Access Management (IAM). MFA provides critical protection against unauthorized access by requiring a second factor for authentication. Adversaries or compromised administrators may deactivate MFA devices to weaken account protections, disable strong authentication, or prepare for privilege escalation or persistence. This rule monitors successful DeactivateMFADevice API calls, which represent the point at which MFA protection is actually removed.

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Identifies sensitive AWS IAM operations performed via AWS CloudShell based on the user agent string. CloudShell is a browser-based shell that provides command-line access to AWS resources directly from the AWS Management Console. While convenient for administrators, CloudShell access from compromised console sessions can enable attackers to perform privileged operations without installing tools or using programmatic credentials. This rule detects high-risk actions such as creating IAM users, access keys, roles, or attaching policies when initiated from CloudShell, which may indicate post-compromise credential harvesting or privilege escalation activity.

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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 a single principal directly invoking AWS Lambda functions at a high volume within a one-hour window. Adversaries may drive excessive invocations to abuse functions for resource hijacking or cryptomining, to inflate costs in a denial-of-wallet attack, or to enumerate function behavior. This is a volumetric heuristic: the threshold is environment-dependent and high-throughput applications can exceed it, so tune it to the deployment. This rule relies on AWS Lambda data event logging, which is not enabled by default.

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Identifies the creation of an AWS Lambda event source mapping, which connects an event source such as an Amazon SQS queue, an Amazon Kinesis or DynamoDB stream, an Amazon MSK or self-managed Apache Kafka topic, or an Amazon MQ broker to a Lambda function so the function is automatically invoked when new records arrive. Adversaries with "lambda:CreateEventSourceMapping" permissions can abuse this to establish stealthy, event-driven persistence and execution, or to continuously siphon records from a stream or queue into attacker-controlled function code. Because the function then runs on its own whenever the source produces events, this grants durable execution without any further interactive activity by the adversary.

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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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Detects when an Amazon Bedrock agent is associated with, or updated to use, a knowledge base via the AssociateAgentKnowledgeBase, or UpdateAgentKnowledgeBase API actions. Bedrock agents consume knowledge base (RAG) content as trusted context for the model. By wiring an agent to an externally controlled or third-party knowledge base, or by swapping in an attacker-controlled knowledge base, an adversary can redraw the agent's trust boundary toward an untrusted source. This is a software-supply-chain compromise and an indirect prompt-injection delivery vector: poisoned or adversarial content served from the associated knowledge base is treated as authoritative by the agent. Validate that the associated knowledge base, and any underlying data source, is owned and controlled by your organization.

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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 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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Detects successful AssumeRoleWithWebIdentity where the caller identity is a Kubernetes service account and the source autonomous system organization is present but not Amazon.com, Inc. EKS workloads that obtain IAM credentials via IAM Roles for Service Accounts (IRSA) normally reach STS from AWS-managed or AWS-associated networks; the same identity from a clearly external ASN can indicate a stolen or misused projected service-account token being exchanged for IAM credentials off-cluster.

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Detects when the AmazonEKSClusterAdminPolicy or AmazonEKSAdminPolicy is associated with a principal via the EKS Access Entries API. This grants full cluster-admin equivalent access to the specified IAM user or role. Unlike the legacy aws-auth ConfigMap which is only visible in Kubernetes audit logs, Access Entries modifications appear in CloudTrail, providing an additional detection surface. Attackers who have obtained IAM permissions to manage EKS access entries can use this API to backdoor cluster access for persistence, mapping attacker-controlled IAM identities to cluster-admin privileges without modifying any Kubernetes resources.

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Detects successful Amazon EKS Access Entries API operations that create, update, attach, detach, or delete authentication mappings between IAM principals and the cluster. Changes to access entries alter who can authenticate to Kubernetes and what Kubernetes-level permissions they receive, without requiring edits to in-cluster RBAC objects. Unexpected callers or timing may indicate persistence or privilege abuse. Common automation identities (service-linked roles, eksctl, Terraform, CloudFormation role patterns) are excluded to reduce noise; tune further for your deployment pipelines.

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Identifies a short sequence of EC2 management APIs against the same instance that is consistent with modifying instance user data and forcing it to run on the next boot: ModifyInstanceAttribute with user data, followed by stop and start. Adversaries may update userData and cycle instance state so malicious scripts execute as root on Linux or as the system context on Windows. This rule correlates successful StopInstances, StartInstances, and ModifyInstanceAttribute events that reference userData within a five-minute window, grouped by instance, user.name, account, source IP, and user agent. A hit requires exactly three distinct API names in that bucket.

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Detects when credentials issued through AssumeRoleWithWebIdentity for a Kubernetes service account identity are later used for several distinct AWS control-plane actions on the same session access key. Workloads that use EKS IAM Roles for Service Accounts routinely exchange a projected service-account token for short-lived IAM credentials; this rule highlights sessions where that exchange is followed by a spread of sensitive APIs—reconnaissance, secrets and parameter access, IAM changes, or compute creation—beyond what routine pod traffic usually shows. High-volume S3 object reads and writes are excluded from the correlation set to reduce noise from normal data-plane work.

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Detects AWS access keys that are used from both GitHub Actions CI/CD infrastructure and non-CI/CD infrastructure. This pattern indicates potential credential theft where an attacker who has stolen AWS credentials configured as GitHub Actions secrets and is using them from their own infrastructure.

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Surfaces an AWS identity whose successful API traffic is dominated by a small set of large cloud-provider source AS organization labels, yet also shows a very small share of traffic from other AS organization names—including at least one sensitive control-plane, credential, storage, or model-invocation action on that uncommon network path with recent activity from the uncommon path. The intent is to highlight disproportionate “baseline” cloud egress versus sparse use from rarer networks on the same principal, a shape that can appear when automation or CI credentials are reused or pivoted outside their usual hosted-cloud footprint.

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Identifies AWS API activity originating from uncommon desktop client applications based on the user agent string. This rule detects S3 Browser and Cyberduck, which are graphical S3 management tools that provide bulk upload/download capabilities. While legitimate, these tools are rarely used in enterprise environments and have been observed in use by threat actors for data exfiltration. Any activity from these clients should be validated against authorized data transfer workflows.

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Detects creation of a new AWS CloudTrail trail via CreateTrail API. While legitimate during onboarding or auditing improvements, adversaries can create trails that write to attacker-controlled destinations, limit regions, or otherwise subvert monitoring objectives. New trails should be validated for destination ownership, encryption, multi-region coverage, and organizational scope.

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Detects deletion of an AWS CloudTrail trail via DeleteTrail API. Removing trails is a high-risk action that destroys an audit control plane and is frequently paired with other destructive or stealthy operations. Validate immediately and restore compliant logging.

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Identifies the execution of Living Off the Land Binaries (LOLBins) or GTFOBins on EC2 instances via AWS Systems Manager (SSM) SendCommand API. This detection correlates AWS CloudTrail SendCommand events with endpoint process execution by matching SSM command IDs. While AWS redacts command parameters in CloudTrail logs, this correlation technique reveals the actual commands executed on EC2 instances. Adversaries may abuse SSM to execute malicious commands remotely without requiring SSH or RDP access, using legitimate system utilities for data exfiltration, establishing reverse shells, or lateral movement.

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Identifies the first time, within the configured history window, that a long-term IAM access key ID (prefix AKIA) is used successfully from a given source.ip in AWS CloudTrail. Long-term access keys belong to IAM users or the account root user. They are a common target after credential theft or leakage, including supply-chain and exposed-key scenarios. Temporary security credentials (prefix ASIA) and console sessions are excluded so the signal emphasizes programmatic access patterns.

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