Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the addition of a new Multi-Factor Authentication (MFA) method for a user account shortly after a suspicious or risky login attempt (e.g., unfamiliar features, risk states, or unusual network locations). This pattern is consistent with an adversary attempting to establish persistent access by registering their own MFA device to a compromised account.
Detects a sequence of events where a user performs a sign-in from an unmanaged or mobile device while interacting with applications often associated with MFA or SSO prompts (indicative of a social engineering lure), followed by an administrative update to register a new authentication method within 30 minutes. This pattern is characteristic of attackers establishing persistent access to an account by adding their own MFA device.
Detects anomalous activity in Azure AD/Microsoft Graph API logs, where a specific user or application performs a high volume of requests across multiple sensitive administrative categories (such as directory roles, authentication methods, applications, and service principals) within a 30-minute window. This behavior is often indicative of reconnaissance or privilege escalation attempts by an adversary who has compromised an identity.
This rule detects the addition of new multi-factor authentication (MFA) methods to an Azure AD or Entra ID user account. Adversaries often register new MFA methods (such as phone numbers or authenticator apps) to accounts they have compromised, enabling them to bypass existing MFA requirements and maintain persistent access to the target environment.
Detects any successful login event for a user in an Azure AD environment. The rule acts as a coarse proxy for potential device-code grant-type authentication, pending the availability of more granular protocol and grant-type fields in the environment's telemetry.
Detects instances where a large volume of data (greater than 5GB) is transferred through an anonymous proxy using specific cloud application IDs. This pattern is indicative of potential data exfiltration via third-party or obscured network channels.
Detects when a user identity in Azure AD has a new software OTP token activated, specifically marked with property flags indicating a placeholder or new device enrollment. This behavior can be indicative of an adversary registering a new, attacker-controlled MFA device to maintain persistent access to a compromised account.
Detects high-volume file download or data access events across SharePoint, OneDrive, and Exchange Online. This rule monitors for requests from Python-based HTTP libraries (httpx, requests, urllib) targeting Microsoft Graph API endpoints, which is a pattern indicative of automated bulk data collection or exfiltration, often observed in campaigns like Storm-3121 or Storm-3032.
Detects a sequence of user activities—authentication, MFA registration, Graph API reconnaissance, and data exfiltration—originating from a rotating set of distinct source IP addresses for the same user within a 24-hour period. This behavior is indicative of coordinated infrastructure rotation tactics used by threat actors in cloud intrusions.
Detects anomalous usage of the OAuth 2.0 Device Authorization Grant flow (Device Code Flow). This flow is frequently abused by attackers to bypass MFA by tricking users into authenticating an attacker-controlled device or session, potentially leading to token theft or unauthorized access to sensitive enterprise applications.
This rule monitors Azure AD Sign-in logs to identify 'impossible travel' scenarios, where a user authenticates successfully from two geographically distant locations within a time window (1 hour) that would require travel speeds exceeding a plausible threshold. This behavior is a common indicator of credential compromise or session token replay attacks.
Detects instances where identified AI agents, bots, or automated assistants perform bulk access operations on mailboxes (e.g., MailItemsAccessed, Send, deletions) or data stores (e.g., FileDownloaded, FileAccessed, SearchQueryPerformed) in Microsoft 365 environments. This monitors for potentially compromised or malicious AI service accounts abusing legitimate access to harvest sensitive data.
Detects instances of OAuth device code flow authentication from suspicious sources (e.g., unfamiliar devices, non-compliant/unmanaged devices, or unusual geographic locations) that are followed closely by an OAuth application consent event. This behavior is indicative of potential consent phishing or device code phishing attacks attempting to gain persistent access to cloud resources.
This rule identifies successful OAuth 2.0 device code authentication attempts originating from both an IP address and country not previously seen for that specific user, using a 90-day baseline that requires at least 5 prior non-device-code sign-ins to be established (reducing noise from sparse-history/new accounts). It excludes known legitimate device-code applications (e.g., Azure CLI, Azure PowerShell, Visual Studio, VS Code) and sign-ins from devices already marked compliant/managed. This tightened logic reduces false positives from routine travel or home/office network changes while still catching device code phishing, where an adversary tricks a user into entering a device code on a malicious site to obtain an access token.
Detects anomalous sign-in behavior shortly after a successful MFA-authenticated session in Microsoft 365. This rule flags scenarios where a subsequent session for the same user occurs within a short window (30 minutes) from a different IP, ASN, User-Agent, or device identifier, potentially indicating the replay of a stolen session cookie obtained via Adversary-in-the-Middle (AiTM) phishing.
Detects high-risk account modifications, such as adding MFA methods or OAuth consent grants, occurring shortly after a user sign-in. This pattern is indicative of post-AiTM session theft (e.g., NovaCookies) where an adversary uses a stolen session token to modify account settings for persistence or MFA bypass.
Detects anomalous reuse of an authenticated Microsoft 365 session cookie shortly after a successful MFA-satisfied sign-in. The detection identifies scenarios where the subsequent access originates from a different IP address, device, or user agent without an intervening MFA challenge, which is a pattern indicative of session hijacking techniques like Adversary-in-the-Middle (AiTM) or session token replay.
Detects anomalous sign-in behavior where a device code authentication flow is initiated from one location, but the resulting session is replayed from a different IP address in a different country within a one-hour window. This behavior is indicative of an adversary capturing an authentication token via a device code phishing or relay attack and attempting to use it from their own infrastructure to bypass conditional access policies and MFA.
Detects inbound emails masquerading as reputable advisory firms (PwC or KPMG) using lookalike sender domains, combined with wire-transfer fraud phrases in the subject line. Correlates with identity logon telemetry for the same fraudulent WhatsApp Business phone number, enabling detection across both email and authentication channels. Note: message body content is not available in the EmailEvents schema, so fraud-phrase matching is subject-only.
This rule monitors for high-volume, suspicious activity involving the enumeration of sensitive cloud resources across multiple Azure services (e.g., Kubernetes cluster secrets, KeyVault secrets, Backup vault items, and DataFactory/Web connections). An alert is triggered when a single identity performs a high number of operations across multiple distinct resource types within a one-hour window, which is indicative of credential harvesting or cloud reconnaissance.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
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