Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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* Compare distances and times of consecutive sign-ins for the same user.
* Flag events where travel speed exceeds physical limits as potential unauthorized access.
Example: Same user signing in from Chennai to Mumbai (India) in 30 minutes triggers an alert.
Distance - 5,560 km.
Required speed - 11,000 km/h
The event is flagged as impossible travel.
* Flag events where travel speed exceeds physical limits as potential unauthorized access.
Example: Same user signing in from Chennai to Mumbai (India) in 30 minutes triggers an alert.
Distance - 5,560 km.
Required speed - 11,000 km/h
The event is flagged as impossible travel.
This detection looks for administrative activity that creates or updates Cross-Tenant Access Settings or Cross-Tenant Identity Synchronization Settings in Microsoft Entra ID (Azure AD). These settings control how identities, claims, and trust relationships are shared between Microsoft Entra tenants.
Detects strong indicators of a successful device code phishing attack.
Where emabeded URL contain googlecontent
https://www.proofpoint.com/us/blog/threat-insight/beyond-credentials-weaponizing-oauth-applications-persistent-cloud-access
Detects a single Gmail sender address using multiple distinct display names within a short timeframe (1 hour). This behavior can indicate an attempt at impersonation, phishing, or spam campaigns where an attacker tries to appear as different entities from the same compromised or controlled email account.
Detects access or search activity in SharePoint or OneDrive involving keywords related to gift cards, IT workflows, and remote access. This is a reconnaissance technique used by the Jingle Thief group (also tracked as Atlas Lion and STORM-0539) to find sensitive internal documentation.
Detects URLs that use the `https://user@host` syntax where the 'user' part is crafted to look like a legitimate domain (e.g., `https://example.com@malicious-site.net`). This is a technique used by the Jingle Thief group (also tracked as Atlas Lion and STORM-0539) to craft convincing phishing links.
Detects a high volume of emails being moved to the 'Deleted Items' folder from the 'Inbox' or 'Sent Items' folders by a single user in a short timeframe. This behavior is a known tactic used by threat actors, such as the 'Jingle Thief' group (CL-CRI-1032/Atlas Lion), to hide malicious activities like internal phishing by deleting the sent messages and any replies.
Detects internal phishing emails with subject lines mimicking IT service notifications, such as from ServiceNow. This is a tactic used by the Jingle Thief group (also tracked as Atlas Lion and STORM-0539) for lateral movement within a compromised organization.
KQL Query from file: Detecting Threat Actor Storm-2657
Detects when Microsoft Cloud App Security (MCAS) reports a risky sign-in attempt due to an impossible travel scenario. This indicates a potential compromise of user credentials where an attacker logs in from a geographically distant location in a short timeframe, making physical travel impossible.
Detects internal phishing emails with subject lines mimicking IT service notifications, such as from ServiceNow. This is a tactic used by the Jingle Thief group (also tracked as Atlas Lion and STORM-0539) for lateral movement within a compromised organization.
Detects when a user successfully logs on to sanctioned applications from an IP address identified as risky by Microsoft Cloud App Security. This indicates a potential compromise of the user's account or an attempt to access resources from an unusual and potentially malicious location.
// Discover email forwarding creation attempts outside of your organization.
Detects agentic AI jailbreak attempts that try to establish persistent control over M365 Copilot through rule injection, universal triggers, response automation, system overrides, and persona establishment techniques. The detection analyzes the PromptText field for keywords like "from now on," "always respond," "ignore previous," "new rule," "override," and role-playing commands (e.g., "act as," "you are now") that attempt to inject persistent instructions. The search computes risk by counting distinct jailbreak indicators per user session, flagging coordinated manipulation attempts.
Detects M365 Copilot users accessing from multiple geographic locations to identify potential account compromise, credential sharing, or impossible travel patterns. The detection aggregates M365 Copilot Graph API events per user, calculating distinct cities and countries accessed, unique IP addresses, and the observation timeframe to compute a locations-per-day metric that measures geographic mobility. Users accessing Copilot from more than one city (cities_count > 1) are flagged and sorted by country and city diversity, surfacing accounts exhibiting anomalous geographic patterns that suggest compromised credentials being used from distributed locations or simultaneous access from impossible travel distances.
Detects M365 Copilot access from non-compliant or unmanaged devices that violate corporate security policies, indicating potential shadow IT usage, BYOD policy violations, or compromised endpoint access. The detection filters M365 Copilot Graph API events where deviceDetail.isCompliant=false or deviceDetail.isManaged=false, then aggregates by user, operating system, and browser to calculate metrics including event counts, unique IPs and locations, and compliance/management status over time. Users accessing Copilot from non-compliant or unmanaged devices are flagged and sorted by activity volume and geographic spread, enabling security teams to identify unauthorized endpoints that may lack proper security controls, encryption, or MDM enrollment.
Detects M365 Copilot users exhibiting suspicious application usage patterns including multi-location access, abnormally high activity volumes, or access to multiple Copilot applications that may indicate account compromise or automated abuse. The detection aggregates M365 Copilot Graph API events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses, number of different Copilot apps used, and average events per day over the observation period. Users are flagged when they access Copilot from multiple cities (cities_count > 1), generate excessive daily activity (events_per_day > 100), or use more than two different Copilot applications (app_count > 2), which are anomalous patterns suggesting credential compromise or bot-driven abuse.
This KQL is focus on gather statistics related to hackers activities over our tenants, such as the number of emails sent tagged as threats. Below, I’ve provided KQL queries to help identify the risky ASNs mentioned at the beggining.
You can adjust the filtering criteria by modifying the line: “| where SuspiciousPercentage > 95 and Total_different_IPs > 10” based on your specific requirements.
You can adjust the filtering criteria by modifying the line: “| where SuspiciousPercentage > 95 and Total_different_IPs > 10” based on your specific requirements.
Detects M365 Copilot users with failed authentication attempts, MFA failures, or multi-location access patterns indicating potential credential attacks or account compromise. The detection aggregates M365 Copilot Graph API authentication events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses and browsers, failed login attempts (status containing "fail" or "error"), and MFA failures (error code 50074). Users are flagged when they access Copilot from multiple cities (cities_count > 1), experience any authentication failures (failed_attempts > 0), or encounter MFA errors (mfa_failures > 0), which are indicators of credential stuffing, brute force attacks, or compromised accounts attempting to bypass multi-factor authentication.
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