Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential malicious email campaigns by identifying when multiple users submit email reports (e.g., phishing or spam) that share common entities such as sender IP, primary sender address/domain, secondary sender address/domain, or subject line. A configurable threshold determines the minimum number of distinct reports required to flag an entity as suspicious.
This detection identifies network traffic to domains explicitly listed as associated with recent phishing campaigns. It specifically looks for these domains within the RequestURL field of logs from Palo Alto Networks or Cato Networks firewalls/proxies.
Detects network connections to the InterPlanetary File System (IPFS) where the URI contains a pattern indicative of an email address. This behavior is associated with recent phishing campaigns that leverage IPFS to host credential harvesting webpages, attempting to collect user credentials.
This rule detects URLs that contain common redirection or link parameters (u=, url=, redirect=, r=, link=, target=, dest=) where the value of the parameter is a long, alphanumeric string that appears to be an encoded domain. This pattern can indicate attempts to obfuscate the true destination of a link, often seen in phishing campaigns or malicious redirection attempts. The rule specifically looks for encoded parameters containing common top-level domains like .com, .net, or .org.
This rule detects instances where a user clicks on a URL identified as potentially malicious (phishing-related) and subsequently has a risky sign-in event in Azure AD. The rule correlates URL click events from email with Azure AD sign-in logs, specifically looking for sign-ins marked as risky or originating from suspicious user agents (e.g., specific axios versions). The phishing URLs are identified by keywords such as 'azureapplicationregistration.pages.dev', 'chrnobinson.com', 'workers.dev', '.pages.dev', '.web.core.windows.net', '/oauth2/v2.0/authorize', 'client_id=', and 'scope=openid'.
This detection identifies network connections to domains that may be impersonating ConnectWise or ScreenConnect for credential harvesting purposes. The logic is based on tactics observed in the MCTO3030 campaign which used domains with specific country-code TLDs or generic "connectwise" and "screenconnect" themes.
The attacker then overlays fake intrusive elements (e.g. cookie banners, popups, or CAPTCHA) so that the user’s clicks fall on the hidden password manager controls, resulting in completing the forms with sensitive information.
This rule detects Tycoon and ODx AiTM phishing activity that bypasses MFA. It matches:
🔹 Network connections to known malicious domains, IP addresses, and URLs.
🔹 Email-URL clicks and Azure AD sign-in events involving those domains or specific malicious application IDs.
🔹 Sign-ins originating from suspect IP addresses or user-agent strings.
🔹 Network connections to known malicious domains, IP addresses, and URLs.
🔹 Email-URL clicks and Azure AD sign-in events involving those domains or specific malicious application IDs.
🔹 Sign-ins originating from suspect IP addresses or user-agent strings.
📌 Description
Threat actors frequently register lookalike domains to support malicious activities such as phishing attacks, drive-by compromises, and command-and-control (C2) operations. This KQL hunting query is designed to help identify potential phishing and typosquatting domains that may be targeting your organization or its third-party vendors.
🔍 Threat Intelligence Feed
The hunting query utilizes data from the SANS Internet Storm Center (ISC) API, which provides access to newly registered domains.
API Endpoint:
https://isc.sans.edu/api/recentdomains/today?json
Use Cases:
Threat intelligence enrichment
Detection of suspicious or malicious domains
Security log correlation
Returned Data Fields:
Domain name
Registration date
IP address
Type
Additional metadata (if available)
Query by Date (Optional):
Example: https://isc.sans.edu/api/recentdomains/2025-07-08?json
🎯 ATT&CK Framework Mapping
Technique: Acquire Infrastructure: Domains
ID: T1583.001
Reference: MITRE ATT&CK
Threat actors frequently register lookalike domains to support malicious activities such as phishing attacks, drive-by compromises, and command-and-control (C2) operations. This KQL hunting query is designed to help identify potential phishing and typosquatting domains that may be targeting your organization or its third-party vendors.
🔍 Threat Intelligence Feed
The hunting query utilizes data from the SANS Internet Storm Center (ISC) API, which provides access to newly registered domains.
API Endpoint:
https://isc.sans.edu/api/recentdomains/today?json
Use Cases:
Threat intelligence enrichment
Detection of suspicious or malicious domains
Security log correlation
Returned Data Fields:
Domain name
Registration date
IP address
Type
Additional metadata (if available)
Query by Date (Optional):
Example: https://isc.sans.edu/api/recentdomains/2025-07-08?json
🎯 ATT&CK Framework Mapping
Technique: Acquire Infrastructure: Domains
ID: T1583.001
Reference: MITRE ATT&CK
This detection identifies when a one-on-one Microsoft Teams chat is initiated with an external domain that has not been observed in previous Teams chats (past 90 days) or recent email communications (past 15 days). This may indicate a new, potentially unsolicited or suspicious contact attempting to engage with an internal user, which could represent a social engineering attempt or early-stage reconnaissance.
This rule detects potential spearphishing attempts where the sender's email address is from Gmail, there are no attachments or URLs, and the subject line of the email matches the recipient's first name. This pattern is often used in highly targeted social engineering attacks to appear legitimate and bypass traditional email security controls.
This query visualises total emails with Phish detections over time summarizing the data daily by Delivery Location.
KQL Query
KQL Query from file: The Hunt for QR Phisher
KQL Query from file: The Hunt for Blob Phishing Mail Domain
KQL Query from file: Hunting malicious oauth grant by phished user
KQL Query from file: Hunting Rockstar 2FA
KQL Query from file: Threat hunting voice phishing for Teams
KQL Query from file: Advanced Vishing KQL Detection
KQL Query from file: Sneaky 2FA MDO Detection
KQL Query from file: Detecting Mamba 2FA phishing-as-a-service
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