Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects exploitation attempts of the Zerologon (CVE-2020-1472) vulnerability using Impacket tools. The rule monitors for unauthorized machine account password resets via Netlogon, execution of common exploitation scripts like zerologon_tester.py or secretsdump.py, and suspicious NTLM anonymous authentication patterns often associated with the exploit.
This rule identifies potential account compromise by detecting successful logins to at least two different SaaS applications monitored by Microsoft Cloud App Security (MCAS) within a 2-hour window, originating from geolocations separated by more than 500 kilometers. This behavior is indicative of credential sharing or account hijacking where an adversary accesses multiple cloud services rapidly from geographically distant locations.
This rule detects successful SSH authentications using the GSSAPI-with-MIC mechanism. This can indicate legitimate user logins or, in some cases, an adversary using valid credentials to access a system via SSH.
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.
This rule detects interactive logons (LogonType 10) where a single account from the same domain logs into multiple distinct devices. This could indicate an attacker using compromised credentials to move laterally within the network or access multiple systems.
Detects potential Adversary-in-the-Middle (AiTM) phishing activity by identifying successful user sign-ins from a new, historically unseen IP address occurring within 30 minutes of a legitimate sign-in from a known IP, indicating the potential replay of a stolen session or refresh token.
Detects instances where a user successfully authenticates from geographically distant locations within a short timeframe (impossible travel) followed by sensitive privileged operations within a 4-hour window.
This rule detects potential Adversary-in-the-Middle (AiTM) session hijacking by monitoring for successful multi-factor authentication (MFA) events on unmanaged or non-compliant devices, followed within 30 minutes by an API request from a geographically distant location. It includes logic to flag suspicious User-Agent strings often associated with automated AiTM tools.
This rule detects potentially malicious credential abuse patterns, such as credential stuffing or brute force attacks, originating from external IP addresses against edge appliance VPN or administrative interfaces. It identifies activity by correlating a high volume of failed authentication attempts followed by a successful login from the same external IP address.
This rule detects successful authentication attempts directed at network devices from sources not explicitly marked as authorized management systems. It identifies users or systems that are not recognized service accounts authenticating against network infrastructure and flags activity that involves multiple unique destination targets as high risk.
Detects Microsoft 365 OAuth access tokens issued via device_code grant — the mechanism abused by the EvilTokens phishing kit — followed within 1 hour by mailbox forwarding rule creation for the same account, indicating persistent BEC access post-compromise.
This rule detects potential Pass-the-Hash (PtH) attacks by looking for multiple successful network logons (LogonType 3) using NTLM authentication for the same user from the same IP address. A count of 5 or more such events is considered suspicious.
This rule detects unusual logon activity for service accounts. It identifies service accounts by looking for a dollar sign ($) at the end of the username. The rule then counts logons, unique IP addresses, and unique computers for these service accounts within one-hour bins. An alert is triggered if a service account has 10 or more logons or logs in from 5 or more unique IP addresses within that hour, indicating potential abuse or compromise.
Detects potential indicators of an SSH/PAM backdoor. This includes three behaviors: 1) SSH-related system processes writing to suspicious file paths (e.g., /tmp, /dev/shm, or hidden files in user directories), 2) PAM sessions immediately followed by suspicious file writes, and 3) successful SSH authentications for usernames previously reported as 'Invalid' by the system, suggesting a tampered PAM module allowing arbitrary authentication.
Detects NTLM-authenticated network logons (Logon Type 3) originating from loopback IP addresses (127.0.0.1, ::1, or 0.0.0.0). This behavior is often associated with NTLM relay attacks or credential reflection bypass techniques where an adversary forces a local service to authenticate to itself to gain unauthorized access.
This rule detects potential exploitation activity related to CVE-2026-45657 involving the Windows Kernel tcpip.sys driver. It monitors for a combination of system crashes (Kernel-Power 41 or EventLog 6008) correlated with Windows Error Reporting (WER) events referencing 'tcpip.sys', or significant spikes in external authentication attempts occurring shortly before a system crash. The rule aims to identify potential remote code execution attempts manifesting as kernel instability.
Detects a multi-stage attack pattern associated with the Qilin ransomware, beginning with suspicious VPN authentication (potential credential stuffing or bypass), moving through lateral movement attempts (SMB/RDP/WMI), and culminating in ransomware behaviors such as shadow copy deletion, mass file encryption, and ransom note creation.
Detects network logon events (Logon Type 3) using NTLM authentication where the source IP address is local (127.0.0.1 or ::1). This behavior can be indicative of attempts to interact with local services or perform lateral movement within the same host using credential-based techniques such as 'Pass-the-Hash' or unauthorized access to local resources.
Detects successful logon events (4624) or special logon attempts (4648) associated with accounts or workstations identified as Qualys scanner agents. This is typically used to identify or baseline vulnerability scanning activity across the environment.
Detects user sign-in activity originating from more than two distinct countries within the same hour, which is often indicative of impossible travel or compromised credentials.
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