Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors DNS queries and network connections for access to a set of known domain names associated with Adversary-in-the-Middle (AiTM) phishing infrastructure. It correlates data from DNS events, device network logs, and virtual machine network connections to identify potential interactions with malicious phishing sites typically used to capture user credentials or session tokens.
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.
Detects the use of the AADInternals PowerShell module for suspicious device registration activities, such as phantom device creation and Primary Refresh Token (PRT) manipulation, which are common techniques used to bypass Conditional Access policies. The rule correlates PowerShell command execution containing AADInternals cmdlets with subsequent Azure AD device registration events originating from the same host within a 15-minute window.
Detects malicious behavior attributed to the Miasma/TonRAT malware, specifically targeting node.js or bun.exe runtime environments. The rule identifies suspicious network connections to command and control (C2) domains, access to sensitive developer credential files (e.g., .npmrc, .aws/credentials), execution of identified credential-harvesting functions in process command lines, and the execution of specific suspicious preinstall scripts.
Detects unauthorized npm package publish events or file uploads associated with compromised maintainer accounts (czirker, llxlr) and specific malicious packages linked to the Miasma Mini Shai-Hulud supply chain attack.
Detects instances where npm or node processes execute potentially malicious scripts (such as 'tools/setup' or '.github/scripts/precheck') during the package installation lifecycle, specifically when operating within a 'node_modules' context. This is indicative of supply chain attacks where malicious actors inject code into project dependencies.
Detects execution activity associated with the Kimsuky NUKESPED loader, specifically identified by the script interpreter spawning processes or making network requests related to specific decoy URLs (NHIS health portal) or opening decoy PDF files from the user's Downloads directory.
This rule detects potential Server-Side Request Forgery (SSRF) probing attempts against web applications where unauthenticated requests contain a URL parameter used to request external or internal resources. It flags high-volume probing activity and correlates the source IP with concurrent anomalous Azure Active Directory (Azure AD) sign-in events from the same source, such as failed logins or high-risk sign-in attempts.
This rule detects potential Server-Side Request Forgery (SSRF) activity by identifying suspicious patterns in web application access logs. It monitors for high-diversity parameter values within URL query strings (e.g., 'url', 'path', 'target', 'file', 'src', 'dest', 'uri', 'redirect') that deviate from normal behavior, combined with indicators like mixed HTTP response codes (2xx and 4xx) and high request volumes from a single source IP to a specific request path. Such behavior is characteristic of an adversary iteratively probing or brute-forcing internal paths or resources via an application-level vulnerability.
Detects Server-Side Request Forgery (SSRF) attempts targeting the Google Cloud Platform (GCP) metadata server (169.254.169.254). The rule inspects RequestURL and RequestContext fields for indicators of metadata API access, URL parameter manipulation, authority confusion payloads, and encoding bypass techniques often used to access cloud instance metadata for unauthorized credential or information harvesting.
This rule detects network connections from internal hosts to identified C2 or phishing infrastructure associated with the threat actor group UNC1151 (also known as Ghostwriter). It monitors both host-based network events (via DeviceNetworkEvents) and centralized network security logs (via CommonSecurityLog) for traffic targeting a curated list of known malicious IP addresses.
This rule detects potential Remote Access Trojan (RAT) activity by identifying specific indicators associated with Nuitka-compiled binaries, specifically the 'chost.exe' filename or paths containing 'win-driver-xd7d'. Additionally, it monitors for suspicious Python-based loader executions ('loader.py') initiated from temporary directories, which is a common persistence or execution technique for such malware.
This rule identifies phishing emails containing multiple distinct redirect hops across various cloud hosting platforms (e.g., Azure Websites, Firebase, CloudFront). It flags emails as suspicious if they contain URLs from multiple monitored cloud platforms, indicating a likely bulletproof redirect chain, and correlates these emails with click data. It elevates the severity for emails sent to privileged user accounts such as admins, executives, or finance staff.
Detects anomalous browser activity where an endpoint retrieves configuration or scriptlets from the BadBlocker-related API, followed shortly by outbound network connections to suspicious domains or the exfiltration of sensitive browser-based session data like document.cookie, localStorage, or sessionStorage.
This rule detects potential command and control (C2) beaconing activity by identifying devices communicating with known suspicious remote IP addresses at regular, repeating intervals. It calculates the time difference between consecutive connections to specific suspicious IPs and identifies devices that establish at least three connections within one-hour bins, adhering to a 1 to 15-minute heartbeat interval.
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'.
Detects instances where rundll32.exe is used to execute a file with a suspicious non-DLL extension (such as .tmp, .dat, .bin, .jpg, etc.) by specifying an exported function via an ordinal number. Adversaries often use this technique to proxy malicious code execution while attempting to evade security tools that only look for standard .dll extensions.
Detects indicators of the MYRA rootkit, specifically the creation of a hidden shared library in /usr/local/lib/, persistence via a shell script in /etc/profile.d/, and the execution of processes utilizing the LD_PRELOAD environment variable to load the malicious library.
Detects anomalous activity associated with information stealing malware, including unauthorized access to browser credential files (Login Data, Cookies), remote thread injection into browser processes, outbound connections to the Telegram API by non-browser processes, and rapid bulk access to multiple sensitive credential or crypto wallet files.
Detects suspicious executions of Python interpreters or Pip package managers originating from common development tools (like VS Code or Node.js) or utilizing temporary file paths. The rule also monitors for suspicious command-line arguments that indicate obfuscation, encoded payloads, or the use of dynamic evaluation functions.
Detects malicious network connections initiated by wscript.exe or cscript.exe, or file creation events involving known LCRYX malware payloads in the C:\Windows\ directory. It also includes network-level detection using DNS and firewall/proxy logs for known LCRYX malicious domains and IP addresses.
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