Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects high-risk GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects the creation or exposure of public repositories, releases, or gists under a personal GitHub account that utilize naming conventions associated with the 'PixelLeak' workaround. This technique allows adversaries to make previously private screenshots, recordings, or internal assets publicly accessible by hosting them in public personal repositories or releases.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects activity from known Storm-3168 (JADEPUFFER) campaign IP addresses across Azure control-plane, sign-in, and App Service logs. The rule monitors for malicious indicators in Azure Activity logs, Azure Sign-in logs, and Service Principal sign-ins, as well as specific sensitive URI paths (e.g., shells, admin login paths) within App Service HTTP logs.
Detects activity from known Storm-3168 (JADEPUFFER) campaign IP addresses across Azure control-plane, sign-in, and App Service logs. The rule monitors for malicious indicators in Azure Activity logs, Azure Sign-in logs, and Service Principal sign-ins, as well as specific sensitive URI paths (e.g., shells, admin login paths) within App Service HTTP logs.
Detects a suspicious sequence of behavior where an Azure service principal authenticates and immediately performs broad reconnaissance (enumeration) across multiple subscriptions and resource types. This pattern is often indicative of an adversary using a compromised service principal credential to map out an environment.
This rule monitors Azure Active Directory service principal sign-in logs to identify authentication attempts from countries or Autonomous System Numbers (ASNs) that have not been observed in the entity's 14-day historical baseline. Such anomalous login behavior may indicate potential account compromise, token theft, or unauthorized use of service principal credentials.
Detects anomalous, high-volume enumeration activity (List/Get/Read operations) performed by a single identity across multiple Azure resource providers within a short time window, indicative of automated reconnaissance by a compromised service principal or managed identity.
Detects anomalous use of an LLM provider API key by identifying successful API requests originating from multiple distinct countries within a one-hour time window. This activity often indicates the potential theft and concurrent unauthorized use of an API token.
Detects the registration of new tools, plugins, or connectors into an AI agent's registry, particularly from unverified publishers or outside of sanctioned deployment windows. The rule also flags cases where these newly registered tools are immediately invoked with sensitive-scope permissions (e.g., admin, secrets, write), which is consistent with indicators of AI agent supply chain compromise or unauthorized tool injection.
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 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.
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 potential session hijacking or malicious OAuth consent grants within Okta. This rule triggers either when an authenticated session start is associated with risky flags (impossible travel, anomalous device/location) or when an OAuth consent grant for 'recall' or cross-tenant functionality occurs shortly after a high-risk sign-in event.
Detects Azure AD sign-in events indicative of 'EvilTokens' phishing-as-a-service tradecraft. The rule monitors for initial token-grant events (device-code authentication or OAuth application consent) and subsequent successful sign-ins using existing SessionIds. This rule is designed as a foundational detection component for identifying session token replay, which allows adversaries to bypass MFA and password requirements by hijacking valid session tokens.
Detects a multi-stage exfiltration sequence consisting of: (1) bulk file archival/compression using common utilities, (2) suspicious OAuth grant or service principal authorization, and (3) abnormal network egress volume to common ports. This pattern suggests an adversary is preparing data, establishing or leveraging an identity/application for cloud access, and subsequently exfiltrating data.
Detects suspicious AI agent behavior by identifying two primary scenarios: 1) The invocation of sensitive tools (e.g., code execution, file deletion) within a short timeframe following a flagged prompt injection or jailbreak attempt in the same conversation; or 2) The invocation of a tool by an AI agent that is not explicitly defined in its allowed tool list for the given workflow.
Detects high-velocity, automated-paced burst of deletion operations spanning multiple distinct Azure resource types (Storage, SQL, Key Vault, Web Sites) within a 15-minute window. This behavior is indicative of agentic AI-orchestrated cloud resource destruction, contrasting with typical administrative lifecycle management.
Detects modifications to Microsoft Entra ID applications or service principals involving the addition or configuration of 'externalauthprovider' redirect URIs or properties. This behavior is indicative of an attacker attempting to establish a malicious federated identity or persistence mechanism by manipulating application configuration to use an adversary-controlled authentication provider.
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