Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where a single Kerberos logon session (TargetLogonId) is utilized to authenticate from multiple distinct source hosts or IP addresses within a one-hour window. This behavior is inconsistent with normal Kerberos authentication patterns and is a strong indicator of Pass-the-Ticket (PtT) activity, commonly associated with tools like Mimikatz or Rubeus where a stolen ticket is injected into multiple sessions or systems.
Detects anomalous authentication patterns where multiple distinct user identities authenticate from the same source IP (often a VPN or proxy) within a short timeframe, characteristic of North Korean IT worker laptop-farm or identity-mule operations where a single operator manages multiple contractor accounts.
This rule detects scenarios where a user completes a device-code authentication flow, followed immediately (within 10 minutes) by the registration of an unmanaged, non-compliant device to their account. This behavior is indicative of potential token theft (e.g., EvilTokens) where an adversary uses a stolen token to register their own device as a persistent, unmanaged entity, bypassing standard corporate device enrollment controls.
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.
Detects a credential stuffing attack characterized by a high volume of failed authentication attempts from a small set of source IP addresses followed by a successful authentication within a short timeframe. This behavior is indicative of an attacker attempting to use valid credentials obtained from infostealer logs or credential leaks to gain unauthorized access to accounts.
Detects instances where a user successfully authenticates via the OAuth Device Code flow using an unmanaged or non-compliant device, followed closely by high-privileged Azure/Graph API administrative actions within a short timeframe. This behavior is indicative of a device-code phishing attack aimed at bypassing MFA controls, including FIDO2/passkeys, by compromising an active session.
Detects instances where a user successfully authenticates via the OAuth Device Code flow using an unmanaged or non-compliant device, followed closely by high-privileged Azure/Graph API administrative actions within a short timeframe. This behavior is indicative of a device-code phishing attack aimed at bypassing MFA controls, including FIDO2/passkeys, by compromising an active session.
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 instances where a successful login occurs on an ESXi/vCenter hypervisor, followed by the creation or power-on of a virtual machine on that same host within a two-hour window. This behavior is indicative of an attacker establishing access to a hypervisor environment to spin up rogue, unmonitored virtual machines to evade detection tools and facilitate further malicious activity.
Detects anomalous service principal activity characterized by multiple, rapidly issued, and overlapping OAuth tokens within a short time window. This pattern is associated with automated/agentic token orchestration (e.g., JADEPUFFER/Storm-3168 campaign) rather than standard manual operator activity, specifically when leveraging the python-requests user agent.
Detects anomalous activity where a successful device-code authentication is immediately followed by rapid multi-device registration or Intune enrollment. This pattern is consistent with GhostCode-style attacks involving Primary Refresh Token (PRT) theft and rogue device registration to bypass conditional access policies.
Detects instances where a privileged account (such as an administrator or helpdesk user) removes MFA security information or resets a password for a target account, followed shortly (within 4 hours) by a sign-in attempt from that same target account exhibiting risk characteristics such as impossible travel or unfamiliar features. This pattern is often associated with social engineering attacks, such as those attributed to Scattered Spider, where an attacker coerces support staff to disable MFA protections for an account.
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 a sequence of events characteristic of exploitation against RouterOS (CVE-2026-67276/CVE-2026-86060), involving a failed SSH login attempt with the username '-2', followed by the creation of a new user account within the same SSH session, and a successful login with that newly created account.
Detects anomalous outbound email activity from an account that recently performed an OAuth device code sign-in characterized by elevated risk levels. The rule identifies potential token abuse by looking for a high volume of emails with either a significant percentage of external recipients or patterns indicative of bulk mailing, common in phishing campaigns that leverage compromised identities (e.g., EvilTokens).
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 Azure AD sign-in events where the user-agent matches common scripting or automation libraries such as python-requests. This behavior is often indicative of automated token replay attacks, where attackers attempt to leverage stolen session cookies or tokens (e.g., following AiTM phishing) via proxy infrastructure.
Detects Azure AD / Entra ID sign-in events that utilize the OAuth 2.0 device code authentication flow. This protocol, designed for input-constrained devices, is frequently exploited by adversary-in-the-middle phishing kits (e.g., EvilTokens) to trick users into authorizing a malicious session, effectively bypassing traditional MFA by obtaining an active session token.
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.
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