Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors for successful and failed user authentication events (Logon events 4624 and 4625) occurring on Saturdays or Sundays. It calculates a risk score based on the volume of authentication attempts and classifies the activity, providing visibility into potential off-hours credential misuse or insider threat scenarios.
This rule detects successful or failed logon attempts originating from IP addresses identified by threat intelligence as associated with botnets, command and control (C2) infrastructure, or proxies. The rule specifically looks for 'LogOn' or 'FailedLogOn' activity types where the source IP's threat intelligence indicator type is 'Botnet', 'C2', or 'Proxy', and where activity insights are available.
This rule detects multiple unauthorized access attempts to files identified as machine learning models, weights, or checkpoints. It specifically looks for Security Event ID 4656 (Auditing of object access) where the object name contains keywords like 'model', 'weights', or 'checkpoint', and the access mask indicates unauthorized access attempts (0x0001 for ReadData or ListDirectory, 0x0002 for WriteData or AddFile). The rule then summarizes these attempts by user, computer, and hour, triggering an alert if 5 or more attempts are made within an hour.
This rule monitors authentication events across various SaaS platforms (Jira, Okta, Azure AD, Google Workspace, Auth0, OneLogin) to detect suspicious activity. It calculates a risk score based on indicators such as impossible travel, high volume of distinct source IPs or geographic locations, and anomalous user agent activity, flagging sessions that exceed a defined risk threshold.
This rule detects multiple (3 or more) logon attempts using the 'RunAs' feature (Event ID 4648) for accounts containing 'Admin' or 'SYSTEM' in their username within a one-hour window. This could indicate an adversary attempting to escalate privileges or execute code as a privileged user.
Detects high frequency of denied Multi-Factor Authentication (MFA) requests for a specific user and IP address, indicating potential 'MFA fatigue' or 'MFA bombing' attacks where an adversary attempts to force a user to approve a push notification or code verification.
This rule detects potential brute-force or credential stuffing attempts against web application authentication endpoints by monitoring patterns of success and failure. It aggregates requests over 5-minute intervals, calculating a risk score based on high volumes of authentication failures relative to successful requests from individual source IPs targeting sensitive API paths (e.g., auth, session, login, token).
This rule detects potential brute-force or credential stuffing attempts against web application authentication endpoints by monitoring patterns of success and failure. It aggregates requests over 5-minute intervals, calculating a risk score based on high volumes of authentication failures relative to successful requests from individual source IPs targeting sensitive API paths (e.g., auth, session, login, token).
This rule detects network connections and user sign-ins originating from IP addresses identified as belonging to Iran. It also identifies sign-ins where the reported country is Iran, but the IP address is not found within the provided Iranian IP list, which could indicate IP geolocation discrepancies or obfuscation attempts.
This rule detects potential brute force or credential stuffing attacks against Azure AD by identifying multiple failed sign-in attempts followed by a successful sign-in from the same user and IP address within a short time frame (1 minute). It specifically looks for scenarios where the successful sign-in occurs after the failed attempts and involves different applications, excluding common Office 365 services to reduce noise.
Detects a sequence of failed authentication attempts potentially indicative of an offline JWT secret brute-force attack (using tools like jwt_tool), followed by a successful authentication from the same source. This pattern suggests the adversary successfully recovered the secret, forged a valid token, and subsequently used it to gain unauthorized access.
Detects high-frequency authentication failure events targeting multiple unique user accounts from the same source IP address within a 10-minute window. This behavior is indicative of password spraying or credential stuffing attacks against Azure Active Directory (Microsoft Entra ID). The rule specifically monitors for common error codes related to incorrect passwords or account lockout conditions.
This rule detects potential SSH brute force attacks by identifying high-frequency authentication failures from sshd logs (more than 10 failures in 5 minutes) and rapid, potentially automated connection attempts on port 22 (more than 20 connections in 1 minute) originating from the same source IP on Linux systems.
This rule detects and summarizes Kerberos authentication ticket requests (EventID 4765) and Kerberos authentication ticket request failures (EventID 4766) by subject username on an hourly basis. A high volume of these events for a single user could indicate credential stuffing, brute-force attempts, or other authentication-related anomalies.
This rule detects an unusually high number of NTLM logon attempts (more than 3 within a 10-minute window) from a single IP address to various accounts or workstations. This behavior is indicative of a brute-force attack or password guessing attempt against network services, particularly when the logon type is network (3) or remote interactive (10) and not from localhost.
Detects the exploitation of a vulnerability in the SimpleHelp OIDC authentication flow where an attacker can register a forged technician account and self-register MFA during the first login, effectively bypassing mandatory multi-factor authentication requirements. The detection looks for specific sequences in server logs involving OIDC attribute fetching and 'New Anon' technician registration tags.
Detects a potential brute force or password spraying attempt by identifying high volumes of NTLM logon failures (Event ID 4625) originating from a single IP address, targeting multiple distinct user accounts within a 10-minute window.
This rule detects potential brute force or password spraying attacks by monitoring high volumes of authentication failures on an endpoint within a 5-minute window. Additionally, it identifies the execution of processes associated with multi-factor authentication (MFA) or single sign-on (SSO) clients, which could indicate an adversary attempting to bypass or manipulate MFA mechanisms.
Detects potential Remote Desktop Protocol (RDP) brute force campaigns by aggregating Windows Event ID 4625 (logon failure) with Logon Type 10. The rule triggers when a significant number of failed authentication attempts against a single target account and host are observed within a 1-hour window.
This rule detects potential brute force attacks or username enumeration by identifying 5-minute windows where multiple distinct user accounts trigger a high volume of failed logon events (Event ID 4625). It summarizes failures by timeframe and flags scenarios where more than 5 unique usernames failed to authenticate, categorizing the severity based on the number of accounts affected.
Detects high volumes of authentication failures (EventID 4625) using sub-status 0xC0000064, which indicates the attempted username does not exist. A high frequency of these events targeting multiple non-existent accounts is a strong indicator of automated username enumeration or credential stuffing.
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