Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 the execution of cloud CLI tools (aws, az, gcloud) using Unix shells (bash, sh) to perform sensitive operations such as listing secrets, keys, or metadata, which may indicate credential discovery or exfiltration.
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 the TeamFiltration/UNK_CondorFiltration spray signature: a large
number of distinct target UserPrincipalNames within a single tenant, each
receiving only a handful of sign-in attempts, spread across many distinct
AWS EC2-hosted source IPs/regions within a short time window. Individual
sign-ins from AWS-hosted IP ranges (VPN gateways, corporate cloud proxies,
remote contractors) are not flagged on their own; the rule requires both
the account-fanout and the IP-rotation dimensions to co-occur for the same
tenant in the same window.
number of distinct target UserPrincipalNames within a single tenant, each
receiving only a handful of sign-in attempts, spread across many distinct
AWS EC2-hosted source IPs/regions within a short time window. Individual
sign-ins from AWS-hosted IP ranges (VPN gateways, corporate cloud proxies,
remote contractors) are not flagged on their own; the rule requires both
the account-fanout and the IP-rotation dimensions to co-occur for the same
tenant in the same window.
Detects a distributed password spraying campaign targeting cloud accounts, characterized by a large number of distinct accounts being targeted simultaneously from multiple AWS-hosted IP addresses, with a low volume of attempts per account per hour to evade standard threshold-based detection.
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.
This rule detects the unauthorized download of complete ML model checkpoint or weight files from a model registry or object storage. It monitors for common model file extensions in download events that are not associated with a legitimate deployment justification or a pre-approved change management window, helping to identify potential intellectual property theft or model exfiltration attempts.
Detects potential poisoning of machine learning training data by identifying unauthorized file modifications to datasets, DVC repositories, or model registries that occur outside of authorized ETL or CI/CD windows.
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.
This rule monitors cloud application events for high-frequency, automated API calls directed at AI/ML inference endpoints. A high volume of queries (over 1000) accessing a diverse range of objects (over 50) within a short window (60 minutes) by a single identity or IP address is flagged. Such behavior is characteristic of model extraction or distillation attacks, where an adversary probes an AI model to replicate its capabilities or internal parameters.
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.
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