Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Microsoft Teams communication (calls, chats, messages) originating from a different tenant where the sender's display name is identified as IT Support, Help Desk, or similar. This behavior is indicative of external actors attempting to impersonate internal IT staff to facilitate social engineering or credential harvesting.
Detects anomalous high-volume file access or download activity in Microsoft 365 (SharePoint or OneDrive) by a single user within a 15-minute window, which may indicate bulk data exfiltration.
Detects anomalous high-volume file access or download activity in Microsoft 365 (SharePoint or OneDrive) by a single user within a 15-minute window, which may indicate bulk data exfiltration.
This rule detects activity associated with the 'Ghostcode' threat campaign by monitoring multiple telemetry sources. It identifies connections to known malicious IP addresses and domains, patterns consistent with phishing kit hosting, and specific URL structures used in credential harvesting or payload delivery. The rule correlates sign-in logs, device network events, DNS queries, and email telemetry to identify potential compromise.
Detects spoofed emails carrying a .patch attachment sent to a GitLab incoming-email merge-request address. Requires Microsoft's composite authentication verdict (CompAuth: fail) -- a lower-noise spoofing signal than raw SPF/DKIM/DMARC -- and excludes sender/recipient pairs already seen corresponding with that token address in the prior 30 days to suppress recurring legitimate contributors.
Detects spoofed emails carrying a .patch attachment sent to a GitLab incoming-email merge-request address. Requires Microsoft's composite authentication verdict (CompAuth: fail) -- a lower-noise spoofing signal than raw SPF/DKIM/DMARC -- and excludes sender/recipient pairs already seen corresponding with that token address in the prior 30 days to suppress recurring legitimate contributors.
This rule detects activity related to specific known fabricated news infrastructure domains and associated social media handles, which are indicative of influence operations. It monitors network connection events, email URL clicks, email communication patterns involving these domains, and process executions that reference these specific accounts or handles.
This rule monitors for network and email activity associated with known fraudulent AI reseller infrastructure. It aggregates data from device network connections, email URL inspections, and email header analysis to identify potential interactions with malicious domains known to facilitate AI-related fraud.
This rule identifies emails containing URLs associated with known suspicious or malicious domains (secboxes.com, msbenefit.com, attcdn.com, brianwilli.com). It filters for instances where the sender's domain is not one of the identified suspicious domains to reduce noise from potential email spoofing or miscategorized communications, flagging potential phishing attempts.
Detects network requests to the Microsoft authentication service specifically targeting the /common/oauth2/deviceauth endpoint, which is used in the OAuth 2.0 Device Authorization Grant flow. While legitimate for device authentication (e.g., CLI tools, smart devices), this flow can be abused by adversaries for phishing attacks to bypass traditional credential-based MFA, allowing them to gain persistent access to a user account once the target authorizes the code.
Detects successful Microsoft Intune enrollment events initiated using the Device Code authentication protocol in Azure AD/Entra ID. This flow is often abused by adversaries to perform OAuth device code phishing, allowing them to gain unauthorized access to cloud resources by tricking users into authorizing a malicious application on their own devices.
Detects HTML phishing lures associated with the GhostCode kit, which utilizes obfuscation techniques such as zero-font-size <ins> tags and injected HTML comments between characters to bypass static and NLP-based security analysis.
This rule detects potential OAuth token theft resulting from device code phishing (e.g., N0va phishkit). It monitors for a successful OAuth device-code authentication followed within 5 minutes by a secondary successful sign-in for the same user from a different IP address, different browser client, or different user agent, which is a common indicator of an adversary-in-the-middle (AiTM) session hijacking attempt.
Detects instances where a service principal or entity identified as an AI agent, bot, or assistant accesses files in SharePoint or OneDrive that are likely to contain sensitive credentials, secrets, or configuration files (e.g., .pem, .pfx, .env, id_rsa).
Detects instances where a user account experiences multiple failed sign-in attempts followed by a successful sign-in from a different, geographically distant location within a short timeframe, indicating impossible travel and potential credential compromise.
Detects downstream delivery of likely AI-paraphrased mass-produced lure content by identifying bursts of near-duplicate subject lines from newly-seen domains with auth failures.
Detects downstream delivery of likely AI-paraphrased mass-produced lure content by identifying bursts of near-duplicate subject lines from newly-seen domains with auth failures.
This rule monitors for user interactions with specific domains and URLs known to be associated with suspicious or malicious activity. It aggregates data from multiple sources, including email clicks (UrlClickEvents), device network activity (DeviceNetworkEvents), device browser events (DeviceEvents), and DNS queries (DnsEvents), to identify potential exposure to these indicators of compromise.
Detects activity associated with the 'BlueKit' session relay tool, which uses the rrweb library to capture and replay user session interactions. This rule identifies web browser network events involving rrweb-related scripts occurring within 10 minutes of a successful user sign-in, indicating a potential Adversary-in-the-Middle (AiTM) phishing attack where credentials and session cookies are being captured.
Detects activity associated with the 'BlueKit' session relay tool, which uses the rrweb library to capture and replay user session interactions. This rule identifies web browser network events involving rrweb-related scripts occurring within 10 minutes of a successful user sign-in, indicating a potential Adversary-in-the-Middle (AiTM) phishing attack where credentials and session cookies are being captured.
Hunts network, HTTP, and email-URL telemetry for known ARToken infrastructure (operator backend IPs and phishing/panel domains). Bounded to a 30-day lookback and tiers matches by confidence: domain hits are high-confidence (attacker-registered infrastructure), while IP hits are medium-confidence since the IPs sit on shared DigitalOcean hosting that can be reassigned to unrelated tenants once ARToken's infrastructure is taken down. Empty/unparsed indicator fields are excluded to avoid spurious matches in the HTTP event parsing branch. No known file hashes are associated with this intel.
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