Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects MFA-fatigue style attacks (push-bombing) against Microsoft 365 accounts. The rule identifies a pattern where a user experiences a high frequency of MFA challenges or denials (at least 5 within a 10-minute window) immediately followed by a successful sign-in from a previously unseen IP and device, excluding phishing-resistant authentication methods and break-glass accounts.
Detects the reuse of a session or refresh token from a geographic location that contradicts recent user activity (impossible travel) without a new interactive MFA challenge. This activity is indicative of Adversary-in-the-Middle (AiTM) phishing attacks, such as those leveraging Evilginx, where session cookies are captured and replayed from attacker infrastructure to bypass multi-factor authentication.
Detects successful authentication events by a service principal (e.g., an AI agent or automated application) that have been flagged by Azure AD Identity Protection as having anomalous risk characteristics, such as unfamiliar locations, unexpected travel patterns, or unfamiliar sign-in features.
Detects successful remote authentication events (Network or Interactive) performed by AI agent orchestration service accounts. This rule monitors for atypical lateral movement patterns where an automated service account pivots to remote hosts using protocols such as RDP or WinRM, diverging from its standard operational context.
Detects potential password spraying activity by identifying high-volume failed authentication attempts (ResultType 50126 or 50053) against multiple accounts from a single source IP, followed by a successful authentication from the same IP. This pattern indicates an adversary who has identified one or more valid credentials after an initial spraying campaign.
Detects a single user account performing NTLM network logons (LogonType 3) to 3 or more distinct hosts within a short time period. This pattern is commonly associated with lateral movement techniques such as Pass-the-Hash, where an attacker uses compromised credentials to spread across a network.
This rule monitors for suspicious activity related to AI service accounts and API keys. It detects potential account or key compromise by identifying impossible travel sign-ins coupled with AI API usage, simultaneous usage of a single API key from multiple disjoint network locations, and sudden, high-volume activity from previously dormant service account API keys.
Detects HTTP requests directed at edge appliances (FortiOS/FortiProxy, SonicWall, Cisco ASA) that match known CVE-associated URI paths, alongside successful administrative authentication to management or VPN interfaces originating from external source IP addresses. This rule is designed to identify potential initial access via vulnerability exploitation or unauthorized administrative login to critical infrastructure.
This rule detects successful user logins from multiple geographical locations (impossible travel) where multi-factor authentication (MFA) was not utilized. This behavior is indicative of credential theft or account takeover attempts.
Detects potential exploitation of Active Directory Certificate Services (AD CS) misconfigurations (specifically ESC1, involving client-auth EKU and enrollee-supplies-subject). The rule correlates certificate issuance events (4886, 4887, 4888) involving sensitive templates or suspicious subject alternative name (SAN) mismatches with subsequent Kerberos PKINIT authentication (4768) events using the issued certificate.
This rule detects potentially malicious activity involving Service Principals in Microsoft Entra ID. It identifies two scenarios: 1) Impossible travel, where a service principal authenticates from two distinct geographic locations within a 60-minute window, and 2) Administrative modifications to a service principal's credentials or authentication methods (e.g., adding secrets, certificates, or MFA methods), which may indicate persistence or credential abuse.
Detects successful VPN authentication events where no multi-factor authentication (MFA) method is reported in the logs. This behavior may indicate an account compromise where an adversary has bypassed MFA or is using a legacy account that lacks MFA enforcement.
Detects a suspicious correlation where an employee's phone number undergoes a SIM change or number reassignment event, immediately followed by a successful login using SMS-based MFA for the same identity. This pattern is indicative of SIM swapping/hijacking attacks, often used by threat actors like Scattered Spider to bypass SMS-based multi-factor authentication.
Detects potential OAuth device code phishing attempts by identifying sign-ins via the device code flow using applications not previously used by the specific user, especially when occurring in a burst pattern across multiple users.
Detects a suspicious sequence of events: an anomalous login (new device, new location, or high-risk sign-in) followed by the assumption of a highly privileged role (via AssumeRole, PIM, or role elevation) and subsequent sensitive administrative actions (e.g., creating access keys, modifying IAM policies, or disabling security auditing). This pattern is indicative of cloud identity compromise and privilege escalation, commonly seen in attacks involving groups like Scattered Spider.
Detects successful VPN logins that bypass multi-factor authentication (MFA) and originate from unusual ASN/IP addresses or occur during atypical hours. This behavior is indicative of potential unauthorized access using compromised credentials, consistent with threat actor patterns that exploit environments lacking mandatory MFA enforcement.
Detects anomalous authentication behavior where the same session token or identifier is utilized by multiple distinct IP addresses or User-Agent strings within a short timeframe. This behavior is indicative of Adversary-in-the-Middle (AiTM) attacks, such as those employing Evilginx, where an attacker intercepts and replays stolen session cookies to bypass standard authentication and MFA controls.
Detects successful administrative logins to VMware ESXi, vCenter, or related VDI infrastructure (Horizon) by users who have recently performed a password or MFA reset. This pattern is indicative of a helpdesk-based social engineering attack (vishing) or credential compromise where the attacker regains account access and immediately pivots to critical virtualization management infrastructure to facilitate ransomware deployment.
Detects successful Entra ID sign-in events that utilize the OAuth 2.0 device authorization grant (device code) flow. This authentication mechanism is frequently abused by attackers to perform device code phishing (e.g., ARToken attacks), where victims are tricked into entering a malicious user code on a legitimate Microsoft login portal to facilitate token theft.
Detects high-volume credential brute-forcing activity targeting Dahua IP camera administrative interfaces (Ports 80/37777). The rule identifies a pattern of multiple connection failures across multiple distinct targets followed by a successful connection, which is highly characteristic of automated credential stuffing or password spraying attacks against embedded IoT devices.
Detects potentially malicious OAuth device code sign-ins by identifying sign-in events that exhibit anomalous characteristics, such as atypical geographic locations, rare client-application combinations, unmanaged/noncompliant device status, or elevated risk signals associated with the account. This detection helps identify account compromise or malicious OAuth consent activity often associated with 'EvilTokens' or device-code phishing attacks.
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