Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects network activity indicating a potential modification or interaction with Microsoft Entra ID (Azure AD) multifactor authentication (MFA) settings. Specifically, it looks for POST requests to the Microsoft Graph API targeting authentication methods (e.g., phoneAuthenticationMethod), which may be indicative of an attacker modifying MFA settings to bypass security controls or register rogue authentication devices.
Detects Azure Privileged Identity Management (PIM) role activations that occur outside of standard business hours (before 07:00 or after 19:00 UTC, or on weekends) or involve high-privilege roles (e.g., Global Administrator, Privileged Role Administrator, Security Administrator).
This rule detects the execution of specific malicious npm packages (jsonwebauth, fake-font) and associated script files (lserver.js, errorHandler.js) linked to Lazarus Group activity. It monitors node.exe processes for suspicious command lines, child processes originating from associated directories, and npm installation activities involving these packages.
Detects instances where a user successfully authenticates using Multi-Factor Authentication (MFA) from one location, followed by a Single-Factor Authentication (SFA) logon from a different location within a 10-minute window. This behavior is indicative of potential session hijacking or session cookie theft where an attacker reuses an authenticated session or manipulates the authentication flow to bypass MFA requirements.
This rule detects suspicious activity within Microsoft Graph API where an account is querying mailbox contents for sensitive payroll and HR-related keywords. This pattern is often observed following an Adversary-in-the-Middle (AiTM) session compromise, where threat actors (such as those associated with the Payroll Pirate campaign) leverage stolen session tokens to exfiltrate sensitive employee information by searching for specific financial and payroll keywords across multiple mailboxes.
This rule detects suspicious activity related to GitHub Actions workflows, including the creation or modification of workflow configuration files, the placement of suspicious JavaScript files in the repository's .github directory, the execution of the 'Bun' runtime within an Actions runner environment, and cloud-based events indicating workflow modification containing 'Run Copilot' strings. These patterns are often associated with CI/CD pipeline compromise, malicious automation, or unauthorized code execution within build environments.
This rule detects suspicious activity related to GitHub Actions workflows, including the creation or modification of workflow configuration files, the placement of suspicious JavaScript files in the repository's .github directory, the execution of the 'Bun' runtime within an Actions runner environment, and cloud-based events indicating workflow modification containing 'Run Copilot' strings. These patterns are often associated with CI/CD pipeline compromise, malicious automation, or unauthorized code execution within build environments.
This rule detects the execution of python.exe or python3.exe using the '-m init' command line flag while attempting to establish a network connection over specific, often non-standard, ports (4444, 1337, 9001, 9002, 5555, 6666, 7777, 8888). This pattern is commonly associated with reverse shells or C2 agent check-ins initiated via Python scripts.
Detects when a single user account performs 5 or more account creations (EventID 4720) or account enables (EventID 4722) within a one-hour period. This activity could indicate malicious behavior, such as an attacker establishing persistence or expanding access, or it could be a sign of automated account provisioning.
This rule detects network communication (connections, DNS queries, or proxy logs) between endpoints and a list of known malicious domains and IP addresses associated with Mistic and Woodgnat/KongTuke threat families. It identifies these activities across network event logs, DNS queries, and security logs.
Detects instances where a guest user is invited to the organization using an email address from potentially malicious TLDs (e.g., .ru, .cn, .ir, .kp) or a domain structure that is represented as an IP address. These patterns are often associated with phishing, reconnaissance, or adversary attempts to gain a foothold in an environment by inviting external identities.
This rule detects processes associated with scripting languages like Python and Node.js that are attempting to access clipboard data or use clipboard-related libraries (e.g., pyperclip, win32clipboard, xclip). This behavior is common in info-stealer malware, such as InvisibleFerret, which uses the clipboard to harvest sensitive user data.
Detects instances where 'MpClient.dll', a legitimate Microsoft Defender component, is loaded by a process that is not a known, trusted Microsoft Defender executable. This behavior can indicate an attempt to abuse Defender's scanning or cleaning APIs for malicious purposes, as seen in 'RoguePlanet' style attacks.
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.
Detects DNS queries and network connection attempts to a set of known malicious domains associated with the threat actor UNC1151 (Ghostwriter). This rule monitors both DNS resolutions and direct network connection attempts to these indicators of compromise (IOCs), which are commonly used in spearphishing campaigns.
This rule detects a high frequency (3 or more within an hour) of command-line executions containing keywords like 'antivirus', 'defense', or 'endpoint' on a Windows system. This activity could indicate an adversary attempting to interact with, disable, or tamper with security software to evade detection.
This rule detects network connections from internal devices to identified APT Command and Control (C2) IP address ranges over common ports (8080, 443, 21, 22, 23, 25, 445). This traffic pattern is indicative of unauthorized external communication commonly associated with malware or adversary activity.
Detects anomalous network communication patterns and file system changes indicative of SoftEther VPN usage, particularly when linked to processes masquerading as VMware or communicating with known malicious C2 infrastructure. The rule monitors for network connections on common SoftEther ports, connections to specific command-and-control IP addresses, and the creation of SoftEther configuration files like 'hamcore.se2' or 'vpn_bridge.config'.
This rule monitors for unauthorized deletion or modification of Microsoft Sentinel alert rules and automation rules. Such actions are indicative of an attacker attempting to impair security monitoring, silence alerts, or disable automated responses to maintain persistence and evade detection.
Detects the execution of processes containing the string 'detect_sandbox' in their command-line arguments. This pattern is commonly used by malicious code or automated tools attempting to determine if they are running within a virtualized or sandbox analysis environment.
Detects network requests to Microsoft Graph API endpoints related to MFA method configuration. This pattern often indicates an adversary attempting to add, modify, or register new MFA authentication methods (such as phone-based methods) for an account to facilitate persistence or bypass security controls.
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