Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
734 detections
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Detects the creation or modification of serverless functions (AWS Lambda, Azure Function Apps, GCP Cloud Functions) that exhibit high-risk configurations. These include functions assigned overly permissive IAM roles, configured for public or unauthenticated invocation, or tied to event triggers commonly abused for persistence in cloud environments.
This rule detects the creation or modification of identity federation trust configurations across multiple cloud platforms (AWS, Azure AD, GCP). Attackers often modify trust relationships to establish persistence, forge credentials, or escalate privileges by introducing malicious identity providers (SAML/OIDC) that grant unauthorized access to the environment.
Detects the initialization of an AWS CloudShell environment or session. Attackers often abuse CloudShell as a managed, agentless environment within the provider's infrastructure to stage tools, perform reconnaissance using the AWS CLI, and exfiltrate data while bypassing standard endpoint-based detection.
Detects unauthorized attempts to modify or delete AWS CloudTrail logs stored in S3, or changes to the S3 bucket lifecycle configuration that would shorten the log retention period. This activity is indicative of an adversary attempting to clear their tracks or hinder audit capabilities.
Detects instances where a principal utilizes the iam:PassRole permission to assign a role with higher privileges to a newly created or updated compute resource (Lambda or EC2), potentially allowing the principal to assume the identity and elevated permissions of the passed role.
Detects modifications to SAML identity providers, federation settings, and trust configurations in AWS and Azure AD/Entra where the signing certificate fingerprint does not match a known-good baseline, and no corresponding change ticket is detected in the raw logs.
Detects modifications to federated domain authentication, identity provider trusts, or federation settings in cloud environments (e.g., Entra ID, Okta). Unauthorized changes to these configurations can be used to establish persistence, enable SAML token forgery, or bypass Multi-Factor Authentication (MFA).
Detects anomalous sign-in activity for service principals (non-interactive accounts) where the same application authenticates from multiple distinct countries within a 24-hour window. This behavior often indicates the unauthorized use of compromised service principal credentials, such as client secrets or certificates, from distributed attacker-controlled infrastructure.
Detects instances where an IAM user or role attempts to assume a role in a different AWS account (Cross-Account) without providing a valid ExternalID. This often indicates unauthorized attempts to access or abuse cross-account trust relationships, which is a common technique for lateral movement or privilege escalation in cloud environments.
Detects AWS CloudTrail events where an actor performs identity or session token manipulation, specifically through the use of sts:GetFederationToken or sts:AssumeRole, or by modifying MFA configurations such as DeactivateMFADevice or CreateVirtualMFADevice on privileged accounts. This activity may indicate an attempt to gain persistent access, bypass security controls, or masquerade as a legitimate principal.
The following analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
The following analytic detects the creation of EC2 instances with previously unseen instance types.
It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded.
This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes.
If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation.
It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded.
This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes.
If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation.
The following analytic identifies cloud instances being modified by users who have not previously modified them. It leverages data from the Change data model, focusing on successful modifications of EC2 instances. This activity is significant because it can indicate unauthorized or suspicious changes by potentially compromised or malicious users. If confirmed malicious, this could lead to unauthorized access, configuration changes, or potential disruption of cloud services, posing a significant risk to the organization's cloud infrastructure.
The following analytic detects cloud provisioning activities originating from previously unseen cities. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs.
The following analytic detects cloud provisioning activities originating from previously unseen regions. It leverages cloud infrastructure logs to identify events where resources are started or created, and cross-references these with a baseline of known regions. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
Detects anomalous, high-volume file deletion, modification, or resource deletion operations performed by an identified AI agent service account within a short timeframe. This behavior is indicative of a compromised AI agent being leveraged for mass data destruction, ransomware activity, or disruptive configuration changes.
Detects the use of open-source secret-scanning tools (e.g., TruffleHog) by identifying their specific User-Agent strings in AWS CloudTrail logs during GetCallerIdentity calls, followed by suspicious enumeration activity (ListBuckets, ListSecrets, ListAccessKeys) from the same source IP. This behavior is indicative of threat actors validating stolen credentials for unauthorized access to cloud resources.
Detects anomalous bursts of sts:GetCallerIdentity API calls, which are often used by adversaries to validate harvested or stolen AWS access keys. The rule monitors for a high frequency of such calls or a large number of distinct access keys being validated from IP addresses that are not part of the organization's approved corporate or cloud egress ranges.
Detects unauthorized modification or deletion of AWS Bedrock model invocation logging configurations. Adversaries may use this technique to disable visibility into LLM usage or redirect logs to an attacker-controlled storage location to hide subsequent malicious activity.
This rule detects anomalous activity involving cloud-based shell services (AWS CloudShell, Azure Cloud Shell, GCP Cloud Shell). It triggers on two main conditions: (1) First-time usage of these shell environments by a principal within the detection window, and (2) The execution of commands associated with data staging, ingress tool transfer, or exfiltration (e.g., curl, wget, storage copy commands).
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