Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a suspected ClickFix social engineering attack where a user is tricked into pasting malicious commands into Windows Terminal, leading to a PowerShell download, followed by the appearance of specific known malicious artifacts (LockScreenContentServer.exe, dui70.dll, or 1.bat) within 15 minutes on the same device.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects a multi-stage process execution chain involving PowerShell: first, a script-based download of remote content, followed by in-memory reflection, and finally, suspicious memory allocation or thread manipulation within the same process context. This pattern is characteristic of fileless malware execution chains designed to evade disk-based detection.
Detects indicators associated with the ChatGPT Custom GPT ClickFix campaign across process, network connection, DNS, and HTTP telemetry: known malicious file hashes, C2 IP addresses, the chattypetty.com domain, and known payload-hosting URLs.
This rule detects potential post-compromise activity related to a campaign using MSP360-masqueraded installers and ScreenConnect remote access tools. It performs an IOC sweep across device file events for known malicious file hashes (associated with installers and post-compromise utilities) and device network events for connections to known malicious domains used for command and control.
This rule detects potential post-compromise activity related to a campaign using MSP360-masqueraded installers and ScreenConnect remote access tools. It performs an IOC sweep across device file events for known malicious file hashes (associated with installers and post-compromise utilities) and device network events for connections to known malicious domains used for command and control.
This rule monitors for known malicious file hashes, command and control (C2) domains, and specific download URLs associated with identified threats. It correlates these indicators across process execution, file activity, and network connection logs to identify potential compromises or malicious activity related to the aware-cr1 infrastructure.
This rule monitors for known malicious file hashes, command and control (C2) domains, and specific download URLs associated with identified threats. It correlates these indicators across process execution, file activity, and network connection logs to identify potential compromises or malicious activity related to the aware-cr1 infrastructure.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
This rule monitors for indicators of compromise (IOCs) associated with known malware and C2 infrastructure, including specific IP:port combinations, malicious domains, URLs for file downloads, and SHA256 hashes of known malware. The rule correlates data across device network events, file events, and process execution events to identify potential infections or communication with malicious infrastructure.
This rule detects known malicious activity including network connections to a specific C2 IP address (69.48.229.140), downloading files from specific C2 URLs, the presence of known malicious file hashes (SHA256), and communications involving specific actor-associated email addresses.
Detects indicators associated with the ChatGPT Custom GPT ClickFix campaign across process, network connection, DNS, and HTTP telemetry: known malicious file hashes, C2 IP addresses, the chattypetty.com domain, and known payload-hosting URLs.
Detects a multi-stage activity where PowerShell is used to download a ZIP file, followed by the execution of a batch script (1.bat) that subsequently launches LockScreenContentServer.exe. This behavior is indicative of a DLL side-loading chain designed to execute malicious code via legitimate processes.
This rule detects potential post-compromise activity related to a campaign using MSP360-masqueraded installers and ScreenConnect remote access tools. It performs an IOC sweep across device file events for known malicious file hashes (associated with installers and post-compromise utilities) and device network events for connections to known malicious domains used for command and control.
Detects the execution of PowerShell with suspicious flags such as encoded commands (-enc, -ec, -encodedcommand) combined with execution policy bypass flags and common .NET classes or methods used for downloading content from the internet (e.g., Net.WebClient, IEX, Invoke-WebRequest). This pattern is commonly observed in fileless malware delivery or secondary payload retrieval stages.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
This rule performs a sweep across device file and network events for indicators of compromise (IOCs) associated with the UAT-11587/Antino campaign. It detects malicious file hashes, specific C2 domain connections, and known lure URLs (HTA/WSF) identified by Cisco Talos.
This rule performs a sweep across device file and network events for indicators of compromise (IOCs) associated with the UAT-11587/Antino campaign. It detects malicious file hashes, specific C2 domain connections, and known lure URLs (HTA/WSF) identified by Cisco Talos.
A July 2026 incident used Polygon smart contracts (EtherHiding) for dynamic payload routing/C2 following a ClickFix/FileFix-style lure driven by compromised WordPress credentials.
The following analytic identifies Living Off the Land Binaries and Scripts (LOLBAS) that can legitimately initiate public network connections but are communicating over uncommon destination ports.
It leverages the Network Traffic data model and applies per-binary common-port exclusions to reduce false positives while preserving suspicious non-standard communication.
This behavior may indicate payload download, command-and-control, proxy execution, or attempts to blend malicious traffic into trusted Windows binaries.
Join this detection with the Process Execution events to provide context and avoid false positives.
It leverages the Network Traffic data model and applies per-binary common-port exclusions to reduce false positives while preserving suspicious non-standard communication.
This behavior may indicate payload download, command-and-control, proxy execution, or attempts to blend malicious traffic into trusted Windows binaries.
Join this detection with the Process Execution events to provide context and avoid false positives.
The following analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443). Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious this indicate potential malware delivery or other nefarious activity.
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