Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects outbound network requests to popular Large Language Model (LLM) API endpoints originating from processes that do not appear to be standard web browsers or identified legitimate development tools. This behavior may indicate an unauthorized script, malware, or compromised process attempting to use LLM APIs for C2, data exfiltration, or automated reconnaissance.
Detects outbound network connections from internal devices to a set of known malicious IP addresses (45.151.45.31 and 46.166.79.31) which may indicate command and control communication or other malicious activity.
This rule detects the execution of suspicious command lines involving 'evilsocket/nyx' and the use of 'iex' (Invoke-Expression) within PowerShell or common download utilities (curl, wget). It also correlates this with network connections to 'raw.githubusercontent.com' fetching 'nyx.ps1', which is a common pattern for downloading and executing malicious scripts in memory.
Detects anomalous post-exploitation behaviors such as mass file modifications, lateral movement (SMB/RDP), or persistence mechanisms (scheduled tasks/services) following ScreenConnect client session activity. This behavior is indicative of potential exploitation of ScreenConnect to deploy ransomware or move laterally within the network.
This rule monitors endpoint telemetry (File, Process, and Network events) to identify activity associated with a pre-defined set of known malicious file hashes, IP addresses, and domains. It correlates these indicators to detect potential malware execution, persistence, or command-and-control communication on monitored devices.
This rule monitors endpoint telemetry (File, Process, and Network events) to identify activity associated with a pre-defined set of known malicious file hashes, IP addresses, and domains. It correlates these indicators to detect potential malware execution, persistence, or command-and-control communication on monitored devices.
Detects network traffic initiated by the WeaselBiscuit loader targeting api.npoint.io, a known free JSON storage service often abused by adversaries as a dead drop resolver for Command and Control (C2) configurations or secondary payloads.
This rule monitors for network connections to known command-and-control (C2) domains or DGA-patterned domains (ending in .icu or .cfd), as well as the execution or file creation of files matching known malicious SHA256 hashes.
This rule detects outbound TLS connections to suspicious domain names characteristic of the Koktevrat malware's DGA. The rule identifies SNI strings that match a specific regex pattern (8-16 alphanumeric characters followed by .icu or .cfd TLDs), which is indicative of automated C2 communication.
Detects anomalous network connections initiated by a node.exe process spawned by npm.exe within 2 minutes of a lifecycle script (preinstall or postinstall) execution. This behavior is indicative of malicious dependency installation where a compromised package attempts to exfiltrate data or fetch additional payloads immediately upon installation.
Detects anomalous network connections initiated by a node.exe process spawned by npm.exe within 2 minutes of a lifecycle script (preinstall or postinstall) execution. This behavior is indicative of malicious dependency installation where a compromised package attempts to exfiltrate data or fetch additional payloads immediately upon installation.
Detects anomalous network connections initiated by a node.exe process spawned by npm.exe within 2 minutes of a lifecycle script (preinstall or postinstall) execution. This behavior is indicative of malicious dependency installation where a compromised package attempts to exfiltrate data or fetch additional payloads immediately upon installation.
Detects indicators of the Bazinga macOS backdoor and Polygon-C2 campaign, including specific command-and-control domains, C2 infrastructure IP addresses, persistence mechanisms via launch agent property lists, and associated cryptographic indicators like file hashes and wallet addresses.
Detects the use of MSBuild.exe to compile and execute inline code or tasks. The rule identifies suspicious command-line patterns (such as Task/CodeTaskFactory), execution from non-standard directories (Temp, AppData, Downloads, etc.), and subsequent network or child process activity originating from these MSBuild instances, indicating potential defense evasion or code execution.
This rule monitors endpoint network events, file operations, and process executions for known malicious indicators (domains and file hashes) associated with Kremlin activity. It detects connections to suspicious remote domains and the existence or execution of specific malicious file hashes.
This rule monitors network traffic for requests directed at a specific domain ('luizestrelhashapr.online') and path ('/google_api/b83fa72d.css'), which is identified in threat intelligence as an indicator of browser history exfiltration.
This rule detects internal network activity (DNS queries, network connections, and security logs) communicating with domains associated with the GhostCode phishing kit. These domains are typically used to host credential harvesting pages or phishing infrastructure.
This rule detects network traffic directed at the Telegram Bot API (api.telegram.org) where the request URI indicates a bot action ('/bot' followed by 'sendMessage') and the HTTP request body contains the keyword 'VIEW'. This pattern is commonly associated with C2 beacons using Telegram as an intermediary service to send status or command results from compromised hosts.
Detects web traffic directed towards Google Apps Script endpoints that exhibit characteristics of malicious activity, specifically targeting browser-side data collection and user interaction lures. The rule identifies potential phishing attempts by correlating access to Google Apps Script pages (e.g., sidebars or add-ons) with external document-based referrers (e.g., Google Docs) or social media referrers, combined with scripts that attempt to exfiltrate user device environment data, location, or cryptocurrency wallet information.
This rule monitors for a variety of indicators associated with malicious activity, including connections to known malicious IP addresses, the presence of specific malicious file hashes or filenames, modifications to Windows system policies (specifically Legal Notice configuration), and changes to Active Directory Group Policy Objects using specified GUIDs.
Detects outbound web or proxy connections to known consumer-grade cloud storage and file-sharing services. This activity is often indicative of data exfiltration, where an adversary uploads staged or archived sensitive data to third-party file hosting services (e.g., Mega, Dropbox, transfer.sh, pastebin) to bypass traditional enterprise exfiltration controls.
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