Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
734 detections
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Detects modifications to AWS S3 bucket Access Control Lists (ACLs) using the 'PutBucketAcl' API call that grant public read or write permissions. This could indicate an attempt to make sensitive data stored in S3 publicly accessible, potentially leading to data exfiltration.
Detects potential privilege escalation where an identity with iam:PassRole permissions deploys or updates compute resources (CloudFormation, EC2, or Lambda) to assume a more privileged IAM role than the user currently holds.
Detects the use of sts:AssumeRole or sts:GetFederationToken to request temporary security credentials in a different AWS account than the one initiating the request. This can indicate cross-account privilege escalation or lateral movement using identity federation or assumed roles.
Detects modifications to AWS S3 bucket Access Control Lists (ACLs) using the 'PutBucketAcl' API call that grant public read or write permissions. This could indicate an attempt to make sensitive data stored in S3 publicly accessible, potentially leading to data exfiltration.
Detects unauthorized attempts to stop, delete, modify, or change event selectors for AWS CloudTrail logs. This activity is indicative of an attacker attempting to disable audit logging to evade detection while performing further malicious actions in the environment.
Detects the use of AWS CloudShell to execute commands that download or install external files (e.g., using curl, wget, pip, or git), excluding those originating from known approved sources such as AWS-owned domains or standard package registries. This behavior may indicate an adversary attempting to download offensive tooling or backdoors into the cloud environment.
Detects 'PutBucketPolicy' API calls in AWS CloudTrail where the policy is modified to grant 'Allow' access to a wildcard ('*') or anonymous principal, without including restrictive conditional requirements such as VPC endpoints or organizational ID constraints. This behavior is a common precursor to data exfiltration from misconfigured S3 buckets.
Detects modifications to AWS EC2 snapshot or AMI attributes that grant cross-account permissions, including making them publicly accessible. This activity may indicate an attempt to exfiltrate volume data, such as credentials, configuration, or database contents, to an external AWS account.
Detects unauthorized attempts to disable, suppress, or modify security monitoring services in AWS, including GuardDuty, Security Hub, and AWS Config. This rule monitors for API calls that effectively reduce cloud visibility or detection capabilities, explicitly excluding actions performed by authorized security-engineering roles.
Detects the creation of recurring EventBridge rules or EventBridge Scheduler schedules configured with cron or rate expressions that trigger Lambda functions or SSM automation documents. This activity is a common method for cloud-native persistence, allowing for durable, recurring execution of malicious code that persists across credential rotations or resource deletions, provided the actor is not a recognized platform-engineering entity.
Detects a rapid burst of read-only IAM and STS API enumeration calls originating from a single source IP address within a one-minute window. This behavior is indicative of automated reconnaissance tools (e.g., enumerate-iam, Pacu) used in the early stages of a cloud attack to identify permissions and potential targets for privilege escalation.
Detects anomalous AWS activity combining bulk secret retrieval from AWS Secrets Manager using the BatchGetSecretValue API with subsequent authentication failures or validation errors in AWS STS. This behavior is indicative of automated credential harvesting pipelines or agentic frameworks rapidly enumerating and testing stolen credentials.
Detects AWS CloudTrail and Azure Active Directory (Entra ID) privilege-escalation actions (e.g., attaching policies, creating access keys, adding roles) performed by principals from rare source IPs or associated with low-frequency administrative actions, indicating potentially compromised or unauthorized account activity.
This rule detects high-risk AWS API calls that are commonly associated with privilege escalation and persistence. It monitors for the creation of overly permissive IAM policies (wildcard permissions), the creation of new access keys for existing users, and suspicious AssumeRole operations.
Detects potential AWS IAM privilege escalation attempts by identifying suspicious modifications to IAM policies, roles, or trust relationships, as well as unauthorized role assumption events. This rule monitors for activities such as attaching high-privilege policies (e.g., AdministratorAccess), creating new policy versions, and modifying trust policies to allow unexpected principals, which are common indicators of post-compromise activity designed to expand access within an AWS environment.
Detects cloud service principals identified as AI agents or assistants performing sensitive API operations such as IAM policy modifications or storage configuration changes. This pattern may indicate that an AI service account has been compromised or misused to perform reconnaissance, privilege escalation, or persistence, exceeding its intended scope of operation.
Detects a suspicious sequence of events: an anomalous login (new device, new location, or high-risk sign-in) followed by the assumption of a highly privileged role (via AssumeRole, PIM, or role elevation) and subsequent sensitive administrative actions (e.g., creating access keys, modifying IAM policies, or disabling security auditing). This pattern is indicative of cloud identity compromise and privilege escalation, commonly seen in attacks involving groups like Scattered Spider.
Detects high-volume, unauthenticated or anonymous requests (GetObject, ListObjects, ListObjectsV2) against AWS S3 buckets. This behavior is consistent with mass enumeration or scanning activities often associated with data exfiltration attempts against misconfigured publicly-exposed storage buckets.
Detects anomalous, high-volume activity by a single identity involving the provisioning of compute-intensive GPU-based EC2 instances or the bulk invocation of LLM models via AWS Bedrock and SageMaker services within a short timeframe. This behavior may indicate account compromise, resource hijacking for unauthorized AI workloads, or crypto-mining.
Detects a sequence where an AWS Secrets Manager secret is retrieved via CloudTrail, followed within 30 minutes by an EDR-monitored process initiating an outbound network connection on a non-standard port (not 80 or 443). This behavior is consistent with an adversary using stolen credentials to pivot into a persistent command-and-control channel.
Detects anomalous, high-frequency enumeration of AI/ML assets such as model registries, container image repositories, and training data buckets. This activity is indicative of reconnaissance for AI supply chain attacks, specifically tracking with MITRE ATLAS AML.T0007 (Discover AI Artifacts), where an actor probes infrastructure to identify targets for model theft, data poisoning, or unauthorized access.
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