Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,247 detections
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Detects a multi-stage network communication pattern characteristic of agent sandbox evasion and subsequent data exfiltration. The rule first identifies an unusual volume of connections to URL-shorteners using encoded fragments in the URI, and subsequently flags calls to common screenshot or rendering services containing 'url=' parameters, suggesting possible pixel-based data exfiltration over a command and control channel.
Detects network indicators associated with the BambooToken C2 infrastructure, including DNS lookups for specific domains, TLS SNI mismatches, non-standard MQTT port usage, and specific command-and-control tasking keywords within MQTT payloads.
Detects network communication associated with the Cyclops Blink modular malware, specifically targeting known Sandworm C2 IP addresses and non-standard ports used for firewall rule manipulation and C2 beaconing. The rule identifies both inbound and outbound traffic, including potential TLS handshakes on specific non-standard ports.
Detects network traffic associated with the RatHat Android RAT, specifically monitoring for HTTP and TLS connections to the command and control (C2) delivery domain 'kingbss.com', as well as detecting characteristic FRP (Fast Reverse Proxy) client login handshake patterns used for tunnel establishment.
This rule detects network traffic containing the specific 'Xwormmm' string in the payload, which serves as a handshake or banner characteristic of XWorm command-and-control (C2) communications.
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 various indicators of compromise (IOCs) including known malicious file hashes, suspicious file names commonly associated with staging or initialization in web directories, and network communication to known malicious domains or URLs. It also monitors process execution command lines for references to these IOCs, excluding common browser processes.
Detects various indicators of compromise (IOCs) including known malicious file hashes, suspicious file names commonly associated with staging or initialization in web directories, and network communication to known malicious domains or URLs. It also monitors process execution command lines for references to these IOCs, excluding common browser processes.
Detects outbound TCP traffic originating from internal network hosts to a known hardcoded AsyncRAT C2 IP address (158.51.122.136). This indicates a successful infection where a host has established communication with the C2 infrastructure.
Detects x47.c botnet compiled outputs x47_bot.exe/x47_bot.dll or references to the x47.c_FF_v4.1 package directory
This rule detects outbound network beaconing from the SCKit malware to known command-and-control (C2) domains hosted on skyleen.fr or to a specific malicious IP address (139.84.223.178). The rule monitors both TLS SNI indicators in HTTPS traffic and direct IP connections to identify compromised hosts communicating with the attacker's infrastructure.
Detects suspicious activity associated with the DarkMe RAT, specifically targeting cryptocurrency wallet data files. The rule correlates file access to known wallet data paths by a hollowed process 'clspack.exe', followed by suspicious outbound network activity originating from that same process, and potentially preceded by WMI-based antivirus enumeration.
Detects the installation of AI-related packages (e.g., mcp, tiktoken_mcp) via common package managers (pip, npm, docker) followed by an outbound network connection from the same host within 10 minutes. This behavior is consistent with supply chain attacks where trojanized AI/MCP components are used to initiate command-and-control communication shortly after deployment.
This rule detects potentially malicious network activity by monitoring for specific query parameters ('bmodule', 'smodule', 'lmodule', 'task', 'upload.php') in web requests or communication with a specific known-malicious IP address ('62.60.226.50'). This pattern is characteristic of C2 (Command and Control) traffic or unauthorized file staging/upload operations.
Detects periodic, repetitive outbound HTTPS network connections from AI agent-runtime environments to domains that are not in the predefined allow-list. This behavior is consistent with C2 beaconing disguised as application API traffic, potentially indicating an AI agent has been compromised or misused for unauthorized external command communication.
This rule monitors for outbound TCP and HTTP traffic to a known malicious infrastructure hosting a fake or compromised 'Corp MDM' (Mobile Device Management) admin panel, which serves as a Command and Control (C2) channel for spyware.
Detects high-volume database or CRM read activity performed by a user, followed by significant outbound network traffic to known public cloud storage, file-sharing, or messaging services. This pattern is consistent with data staging and exfiltration behaviors often attributed to the ShinyHunters threat actor group.
Detects a sequence of Tox P2P protocol packets consisting of a Cookie Request, Cookie Response, and Crypto Handshake within a single network flow. This pattern is indicative of the Tox P2P protocol, which is sometimes abused by malware (e.g., AvisLoader) for command-and-control (C2) communications to evade traditional network inspection.
Detects Node.js processes exhibiting network activity patterns consistent with 'EtherHiding' style C2 infrastructure, characterized by multiple distinct connections to external WebSocket hosts on non-standard TCP ports within a short timeframe, potentially following interaction with a blockchain/JSON-RPC endpoint.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
This rule detects network connections from internal devices to a list of known malicious domains and IP addresses. These indicators are commonly associated with command and control (C2) infrastructure or malicious activity, and alerting on these connections can help identify compromised systems within the environment.
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