Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects the creation or modification of inbox rules (e.g., forwarding, redirecting, deleting, or moving to hidden folders) that target finance, invoice, or payment-related communications, occurring shortly after an initial user sign-in from a new or unseen country/ASN within the last 30 days. This pattern is indicative of a post-compromise activity where an adversary attempts to exfiltrate financial data or hide malicious email activity.
Detects a sequence of suspicious activities associated with EvilTokens-style session hijacking. This includes the identification of non-interactive sign-in events involving the redemption of a refresh token without secondary MFA, followed by the creation or modification of Exchange Online mailbox rules to facilitate unauthorized email forwarding or exfiltration.
Detects the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
Detects the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
Detects high-privilege OAuth application consent grants occurring shortly after a user identity recovery event (e.g., password or MFA reset). This pattern is indicative of a help-desk social engineering attack where an adversary uses an identity reset to gain access to an account and subsequently establishes persistent access via a malicious OAuth application.
Detects the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
Detects high-privilege OAuth application consent grants occurring shortly after a user identity recovery event (e.g., password or MFA reset). This pattern is indicative of a help-desk social engineering attack where an adversary uses an identity reset to gain access to an account and subsequently establishes persistent access via a malicious OAuth application.
Detects malicious activities associated with the SilkParasite campaign targeting Redis servers. The rules identify the initiation of a 'SLAVEOF' command to a rogue replication host, subsequent handshake (PSYNC/REPLCONF) with an external attacker-controlled node, and the modification of server configuration ('CONFIG SET dbfilename') to potentially facilitate unauthorized file persistence or data exfiltration through the Redis replication mechanism.
Detects instances where a user grants high-privilege permissions (Mail.Read, Files.ReadWrite.All, offline_access) to an OAuth application within 24 hours of a risky sign-in event. This pattern is commonly observed in AiTM (Adversary-in-the-Middle) or consent-phishing attacks designed to establish persistent access to cloud resources.
Detects modifications or creation of GitHub Actions workflow files containing references to Model Context Protocol (MCP) extensions or AI-related build tools. This monitors for indicators of potential software supply chain tampering, where an adversary injects unauthorized CI/CD pipeline steps for malicious execution.
This rule detects potential CI/CD security compromises in GitHub by monitoring for modifications to workflow permissions that grant 'write-all' access, access to sensitive workflow secrets via 'pull_request_target' from forked repositories, and the registration of new self-hosted runners. These actions are common indicators of supply chain attacks aimed at poisoning pipeline execution or exfiltrating build secrets.
Detects the creation or modification of Microsoft Power Automate flows that integrate internal Microsoft 365 services (SharePoint, OneDrive, Exchange) with external communication or exfiltration targets (HTTP requests, external email addresses, or common external web services). This behavior is often indicative of automated data exfiltration.
Detects the creation or modification of Microsoft Power Automate flows that integrate internal Microsoft 365 services (SharePoint, OneDrive, Exchange) with external communication or exfiltration targets (HTTP requests, external email addresses, or common external web services). This behavior is often indicative of automated data exfiltration.
Detects the creation or modification of Microsoft Power Automate flows that integrate internal Microsoft 365 services (SharePoint, OneDrive, Exchange) with external communication or exfiltration targets (HTTP requests, external email addresses, or common external web services). This behavior is often indicative of automated data exfiltration.
Detects the creation or modification of Microsoft Power Automate flows that integrate internal Microsoft 365 services (SharePoint, OneDrive, Exchange) with external communication or exfiltration targets (HTTP requests, external email addresses, or common external web services). This behavior is often indicative of automated data exfiltration.
Detects instances where an Entra ID application or service principal has new secrets or certificates added by a user who is not a registered owner of that application. This behavior is a common technique for establishing stealthy persistence in a cloud environment.
Detects the creation or modification of Microsoft Power Automate flows that integrate internal Microsoft 365 services (SharePoint, OneDrive, Exchange) with external communication or exfiltration targets (HTTP requests, external email addresses, or common external web services). This behavior is often indicative of automated data exfiltration.
Detects anomalous mass consent grants to OAuth applications by users within the environment. This rule monitors for high-risk permissions such as Mail.Read and Files.ReadWrite.All, which are commonly used in consent phishing and application-based persistence campaigns to gain unauthorized access to mailbox and file resources.
The following analytic identifies the use of the esxcli system auditrecords commands, which can be used to tamper with logging on an ESXi host.
This action may indicate an attempt to evade detection or hinder forensic analysis by preventing the recording of system-level audit events.
This action may indicate an attempt to evade detection or hinder forensic analysis by preventing the recording of system-level audit events.
Detects anomalous configuration of Android devices by identifying instances where an accessibility service is granted combined with the enabling of wireless ADB debugging or ADB pairing code extraction. This behavior is often associated with malicious applications seeking unauthorized control or remote access to the device.
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