Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Microsoft Entra ID (formerly Azure AD) sign-in attempts that use the specific, hardcoded User-Agent string associated with the TeamFiltration tool. This tool is frequently used for mass credential testing against Microsoft 365 environments. The detection looks for a suspicious and outdated combination of Teams, Electron, and Chrome versions that would not be present in legitimate, modern Microsoft Teams client traffic.
Detects potential MFA push fatigue (bombing) attacks by identifying users who receive an abnormal frequency of MFA push notification denials or interruptions within a short window, followed immediately by a successful authentication on a previously unseen device fingerprint. This pattern is characteristic of attackers attempting to wear down users into approving unauthorized login requests.
This rule detects a potential brute force attack against the Microsoft SQL Server 'sa' administrative account. It identifies a high volume of failed authentication events (Event ID 18456) for the 'sa' account within a 10-minute window, followed by a successful authentication event (Event ID 18453 or 18454) from the same source IP address. The rule specifically filters for successful logins originating from non-private (internet-facing) IP addresses, suggesting an attempt from an external threat actor.
This rule detects potential brute force activity against network infrastructure devices (firewalls/UTMs). It identifies a sequence where a specific Source IP records multiple failed authentication attempts within a 1-hour window, followed by a successful authentication attempt from the same IP address. This pattern suggests a successful compromise of an account via credential guessing.
Detects a sequence of authentication activity indicative of a credential stuffing attack. The rule identifies a burst of failed password-only authentication attempts followed by a successful authentication for the same account. It requires that the IP address used for the successful login overlaps with the set of IPs that initiated the failed attempts, effectively filtering out typical user noise and highlighting potentially malicious activity where an attacker has identified a valid credential.
Detects a sequence of multiple RDP authentication failures (Event ID 4625, Logon Type 10) from a specific source IP, followed by a successful RDP authentication (Event ID 4624, Logon Type 10) from the same source IP. This pattern indicates a successful credential brute force or password spraying attack against RDP, which is a common initial access vector for ransomware and lateral movement.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
Detects potential exploitation attempts targeting CVE-2026-16232 in Check Point SmartConsole. The rule identifies specific authentication error messages indicating that a username length validation vulnerability is being triggered by a remote attacker attempting to bypass authentication.
Detects potential account compromise by aggregating multiple high-fidelity indicators of adversary activity for a single user within a 72-hour window. The rule triggers when at least three distinct stages of an attack chain are identified, specifically: credential theft/infostealer activity, anomalous sign-in (token reuse/impossible travel), mailbox or OAuth configuration changes, and mass data exfiltration.
The following analytic identifies instances where users may have mistakenly entered their passwords in the username field during authentication attempts. It detects this by analyzing failed authentication events with usernames longer than 7 characters and high Shannon entropy, followed by a successful authentication from the same source to the same destination. This activity is significant as it can indicate potential security risks, such as password exposure. If confirmed malicious, attackers could exploit this to gain unauthorized access, leading to potential data breaches or further compromise of the system.
Detects potential password spraying activity in Azure Active Directory by identifying a single source IP address that authenticates against multiple distinct user accounts within a short time frame. The rule flags instances where there are numerous authentication attempts across many users, characterized by a low per-user failure count followed by at least one successful authentication, which is indicative of a successful password spray attempt.
Detects potential credential stuffing activity by monitoring for a high volume of HTTP 401 (Unauthorized) or 403 (Forbidden) responses originating from a single source IP address within a 60-second window, specifically targeting URIs containing the path 'login'.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
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 high-volume SSH authentication failures from a single source IP address within a short time frame, which is indicative of credential spraying or brute force activity.
This rule detects a password-spraying attack pattern directed at privileged or manager-titled accounts. It monitors for multiple failed sign-in attempts from a single source IP address against a large volume of distinct user accounts identified as managers or senior staff. The detection logic filters for low-frequency attempts per account, which is indicative of a distributed spray attack designed to evade account lockout thresholds while focusing on high-value targets.
This rule detects potential RDP brute force and password spraying attacks by correlating Windows Event ID 4625 (failed logins) and 4624 (successful logins) via RDP (Logon Type 10). It monitors for high volumes of failed attempts across multiple accounts or high volume of failed attempts from a single source, followed by successful authentication from the same source.
Detects sign-in patterns indicative of Adversary-in-the-Middle (AiTM) phishing attacks such as EvilProxy or Tycoon2FA. The rule identifies a sequence where a user fails an MFA challenge (using codes 50074 or 50140) followed by a successful authentication within a short period (1 hour) where the MFA claim appears as 'Previously satisfied' or 'already succeeded' from a different IP address or user-agent string, suggesting the session was stolen or replayed.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
This rule detects potential password spraying attacks against PeopleSoft or PeopleTools applications. It monitors for a pattern where a single source IP address generates multiple failed authentication attempts against a variety of distinct user accounts, followed by at least one successful login from the same source IP. This behavior is indicative of an adversary attempting to gain unauthorized access by iterating through a list of usernames with common or guessed passwords.
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