Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors for two distinct suspicious activities: 1) Inbound emails where the sender display name matches a list of executive titles, commonly indicative of Business Email Compromise (BEC) or executive impersonation attacks; 2) The creation of cloud service accounts with names mimicking administrative or IT support roles, which may indicate an adversary establishing persistent, privileged access.
Detects successful authentication to Atlassian Jira from an anomalous network source (e.g., proxied or unusual geolocation/ASN) following a period of credential inactivity. This activity is consistent with tactics used by the HellCat ransomware group, who leverage credentials harvested by infostealers (such as Raccoon, Redline, StealC, or Lumma) to gain initial unauthorized access to corporate Jira environments.
Monitors GitHub audit logs for suspicious or high-risk activities within CI/CD pipelines, including actions triggered by forks, workflow approvals, branch policy overrides, self-hosted runner registration, and sensitive secret access.
This rule detects suspicious sign-in patterns indicative of an EvilTokens-style Microsoft Device Code OAuth phishing attack. It identifies successful device code flow sign-ins where Multi-Factor Authentication (MFA) was not applied or satisfied, suggesting an adversary has bypassed MFA using a stolen device code. The rule aggregates these suspicious sign-ins by user, listing associated applications, IP addresses, and locations for analyst review.
Detects spear-phishing emails associated with Operation DragonReturn, which impersonates the Indian Income Tax Department. The rule identifies emails containing malicious PDF attachments (e.g., 2026-0610-1.pdf) or directing users to the known phishing domain 'govtop.one', commonly used to harvest credentials or deliver malware.
Detects anomalous network traffic patterns indicative of two distinct techniques: P2P injection into Microsoft 365 Copilot search queries and Server-Side Request Forgery (SSRF) leveraging Bing's image search functionality for data exfiltration.
This rule detects potentially malicious activities involving Microsoft Copilot and Microsoft Graph, specifically focusing on unauthorized mailbox access via non-interactive sessions and patterns indicative of Server-Side Request Forgery (SSRF) during search operations. It monitors for anomalous interaction with the Graph API, specific Copilot search parameters, and suspicious image search requests.
Detects suspicious app installations, custom app creation, and bot additions within Microsoft Teams. This rule identifies activities such as sideloaded app installations, which can bypass standard vetting processes and serve as a persistence mechanism for attackers. It also flags custom app creation outside of typical development teams, which could indicate the deployment of command and control (C2) bots or data exfiltration applications. Additionally, the rule looks for the addition of bots to teams and suspicious keywords in app names that might suggest impersonation or malicious tooling.
Detects rapid high-volume file and messaging operations across multiple Microsoft 365 services (SharePoint, OneDrive, Teams, Exchange) within a compressed 10-minute window from a single identity and IP address. This behavior can indicate data exfiltration or reconnaissance by an adversary.
Slack Anomalous Activity Detected
Cortex XDR
This rule detects anomalous activity reported by Slack's internal anomaly detection system. This includes events such as impossible travel, new device access, suspicious IP changes, and other behavioral anomalies that could indicate a compromised account or unauthorized access to a Slack workspace.
Slack Anomalous Activity Detected
Cortex XDR
This rule detects anomalous activity reported by Slack's internal anomaly detection system. This includes events such as impossible travel, new device access, suspicious IP changes, and other behavioral anomalies that could indicate a compromised account or unauthorized access to a Slack workspace.
Detects an account granting offline access, mail, files, directory, or user impersonation permissions shortly after anomalous authentication. This rule is designed to identify potential persistence mechanisms established by adversaries leveraging OAuth applications, as seen in campaigns like Tycoon 2FA AITM.
Detects the use of weaponized GitHub Actions workflows (e.g., impersonating 'Run Copilot' or 'Dependabot Updates') designed to exfiltrate CI/CD secrets via artifacts, as observed in the Miasma/Mini Shai-Hulud supply chain campaign.
Detects MFA authentication success from one IP address followed immediately by a session access attempt from a different IP address. This pattern is characteristic of Adversary-in-the-Middle (AiTM) phishing attacks, such as those using EvilProxy, Evilginx2, or similar phishing-as-a-service (PhaaS) platforms, where an adversary proxies the authentication flow to capture session tokens.
Detects anomalous bulk publication of NPM packages by a specific compromised maintainer account (czirker) targeting the LeoPlatform and LeoInsights organizations, indicative of malicious propagation behavior similar to the Miasma worm.
Detects potential Browser-in-the-Middle (AiTM) credential relay attacks where a user clicks a malicious phishing URL, followed shortly by a successful interactive sign-in from a different IP address and a previously unseen device.
Detects instances where a single authentication correlation ID is associated with multiple distinct IP addresses within a 10-minute window, followed by high-risk administrative or data-access actions within 30 minutes. This behavior is indicative of session hijacking or post-authentication account compromise, where an adversary uses stolen session tokens to perform unauthorized activities.
This rule detects when a non-administrative user grants an application high-privilege OAuth scopes (such as mail reading, file access, or directory management) within an Azure/Microsoft 365 environment. Such consent grants can be used by attackers to maintain persistence and bypass MFA by gaining delegated access to sensitive resources.
This rule detects a potential data collection or exfiltration attempt in Microsoft Teams by identifying accounts that have accessed an abnormally high number of messages (more than 100 within a 5-minute window). This behavior may indicate an automated process or a compromised user account attempting to scrape internal communications.
This rule detects sensitive modifications to payroll-related information in Workday, such as bank account details, direct deposit settings, or payment method changes. It flags instances where such modifications occur from multiple IP addresses for the same user account, which may indicate account compromise and unauthorized attempts to divert funds.
Detects anomalous automated enumeration of Microsoft Graph API user objects followed by keyword-based searches for sensitive HR, finance, and payroll-related information. This pattern is characteristic of post-compromise reconnaissance activities where an attacker leverages an existing session token to identify targets for further data exfiltration.
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