Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects the 'auto-exfil' pattern associated with the TeamFiltration tool. The rule identifies a sequence of SharePoint Online file access immediately followed by a Microsoft Graph token request from the SharePoint Online Web Client Extensibility app. To reduce noise, it mandates a very short time delta between events (<= 60 seconds) combined with either the use of an IP address/ASN not previously seen for the account in the last 14 days, or a preceding failed sign-in attempt for the account.
Detects a suspected account compromise sequence where an account first authenticates to a Microsoft Teams application from infrastructure associated with TeamFiltration (e.g., specific AWS ranges), followed by a rapid (within 2 minutes) authentication to a VPN or SAML service provider from a different IP address, indicative of an account pivot.
The following analytic identifies when an O365 email account sends and then hard deletes an email to an external recipient within a short period (within 1 hour).
This behavior may indicate a compromised account where the threat actor is attempting to remove forensic artifacts or evidence of exfiltration activity.
This behavior is often seen when threat actors want to reduce the probability of detection by the compromised account owner.
This behavior may indicate a compromised account where the threat actor is attempting to remove forensic artifacts or evidence of exfiltration activity.
This behavior is often seen when threat actors want to reduce the probability of detection by the compromised account owner.
This rule detects a multi-stage Business Email Compromise (BEC) attack involving executive impersonation via look-alike domains, followed by the creation of suspicious inbox rules (forwarding/deletion) by the victim, and concurrent external communication (Teams/Zoom meeting invites) from a newly created account. It correlates these activities to identify sophisticated payment fraud campaigns.
This rule normalizes common Azure AD sign-in fields such as user principal names, source IP addresses, application display names, and authentication requirements for consistent monitoring and analysis.
Detects the reuse of a session or refresh token within a short timeframe (<= 900 seconds) where the source IP location, device identifier, or User-Agent string has materially changed. This behavior is indicative of Adversary-in-the-Middle (AiTM) attacks, where a session cookie has been stolen and replayed from an attacker's infrastructure to hijack a legitimate user's session.
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
This rule detects potentially malicious search activity in Microsoft SharePoint by monitoring for high-frequency queries that include specific site class patterns ('STS_Site' or 'STS_Web') and a document ID range search pattern ('indexdocid'). This behavior is characteristic of an adversary attempting to enumerate or scrape documents within a SharePoint environment.
Detects automated mailbox monitoring (mailbox access cadence) followed by searches for sensitive finance-related keywords (e.g., invoice, payment, wire, ACH). This pattern is characteristic of a 'Box Monitor' or 'Inbox Monitor' actor, which maintains persistence via API polling to exfiltrate financial information from a compromised account.
Detects evidence of account persistence and potential mailbox manipulation following an anomalous sign-in event in Google Workspace. The rule identifies suspicious post-compromise activity such as the addition of new MFA methods, new OAuth application grants, or mailbox forwarding/inbox rules, which are consistent with the reuse of captured authenticated sessions.
Detects sign-in events within Microsoft Entra ID (formerly Azure AD) that utilize the 'deviceCode' authentication protocol. This OAuth 2.0 flow is designed for devices with limited input (e.g., smart TVs, IoT devices, CLI tools) but is frequently abused by attackers in 'device code phishing' campaigns to obtain access tokens by tricking users into entering a code on a malicious site.
Detects potential Adversary-in-the-Middle (AiTM) activity by identifying a successful interactive MFA-protected sign-in followed within 15 minutes by a non-interactive (refresh token) sign-in for the same user from a different IP address, ASN, or User-Agent. This pattern is indicative of session hijacking where an attacker reuses a stolen session token.
Detects a sequence of events where a user with a high-risk or compromised sign-in status registers a new device in Entra ID shortly after the event, followed by subsequent sign-ins from that device that bypass multi-factor authentication (MFA) via Conditional Access policies.
Detects MFA relay phishing kits (such as Bluekit) where the MFA challenge is satisfied by an attacker-controlled proxy rather than the legitimate user device. The rule identifies sign-in attempts where the primary authentication request originates from one network location, while the MFA completion step originates from a different network location, and the MFA claim indicates it was satisfied via a proxied token.
Detects the registration or modification of multi-factor authentication (MFA) security information in Entra ID or Okta that occurs within 15 minutes of a sign-in event flagged as risky. This behavioral pattern is a common indicator of persistence being established by an attacker after performing an adversary-in-the-middle (AiTM) attack, where they leverage a stolen session to register their own MFA factors to maintain long-term access.
Detects high-frequency authentication attempts originating from user agents associated with automation frameworks (Selenium, Playwright, Puppeteer, etc.). This pattern is often indicative of automated credential stuffing, brute force attacks, or CAPTCHA/2FA bypass attempts orchestrated by headless browsers.
Detects high-privilege user accounts (e.g., executives, administrators) undergoing a credential or MFA reset, followed by an atypical sign-in event within a two-hour window. This pattern is indicative of potential account takeover or identity-compromise scenarios, often associated with help-desk vishing attacks where an attacker induces a user or administrator to reset credentials or MFA methods.
Detects MFA or credential reset activities on high-value or privileged accounts that lack a corresponding out-of-band verification signal, such as manager approval or identity proofing, within a one-hour window. This behavior is indicative of social engineering attempts where attackers manipulate help-desk procedures to bypass identity protections.
Detects instances where a user authenticates via the OAuth 2.0 device code flow followed shortly by a new device registration. The rule flags registrations where the device name shape is uncommon compared to historical data or where there is a mismatch between the IP address used for the authentication and the registration, indicating potential device code phishing or account takeover.
Detects anomalous sign-in patterns indicative of real-time MFA relay attacks, where a user principal authenticates successfully from multiple distinct IP addresses and geographical locations within a short time window (15 minutes), suggesting the active interception of credentials and MFA challenges by a Phishing-as-a-Service platform.
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