Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,627 detections
Filters
Last updated
All Time
Detection languages
1,752
374
349
273
255
Contributors
908
661
593
359
164
Categories
1,489
1,153
779
528
515
Platforms
39,915
7,245
6,529
3,864
3,627
Products / Services
802
512
432
340
298
MITRE Techniques
1,179
573
523
506
438
CVEs
17
16
9
8
7
IDS Classtypes
41
15
10
6
5
IDS Protocols
47
24
3
2
1
Detects SSH client processes linked to libssh2 (such as ssh, curl, sftp, scp) exhibiting sustained high CPU usage (>=95%) for more than 60 seconds while maintaining an active outbound SSH connection. This behavior is consistent with the denial-of-service vulnerability CVE-2026-55199, where an attacker-controlled SSH server provides an excessively large extension count during the key exchange process, triggering an infinite loop within the libssh2 library.
Loader used in click-hijacking traffic distribution scheme
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 a suspicious sequence of events involving the creation of an .amazonq/mcp.json file shortly after a git clone operation, followed by an AI coding assistant (Amazon Q, VS Code, Cursor, Windsurf) spawning runtime interpreters (node, npx, python). This behavior aligns with potential exploitation attempts against AI-assisted development environments, such as CVE-2026-12957, where malicious configuration files are used to trigger unauthorized code execution.
Detects the exploitation of CVE-2026-20253, where the Flowise application fails to correctly sanitize or validate environment variables. By using a case-insensitive bypass (e.g., lowercase 'node_options'), an attacker can cause a spawned Node.js child process to honor the variable and execute unauthorized JavaScript files via the '--require' command-line argument.
Detects the spawning of potential Model Context Protocol (MCP) server processes immediately following or by package installation commands (npm, pip). This behavior is characteristic of malicious packages or typosquatted dependencies attempting to register or execute a malicious MCP server configuration upon installation in a developer environment.
This rule detects indicators of activity associated with a Firefox smart_window PoC, specifically designed to exfiltrate private browsing URLs. It monitors for suspicious network connections from the Firefox process to port 8765, the creation of PoC-related scripts, and the execution of associated Node.js processes or command lines.
Detects a potential exploitation of CVE-2026-12957, where a malicious git repository contains an MCP (Model Context Protocol) configuration file. When this workspace is opened, the Amazon Q Developer extension for VS Code improperly processes this configuration, resulting in the unauthorized execution of shell processes (cmd.exe, powershell.exe, bash, or sh) by the extension's host processes (code.exe or node.exe).
Detects Python processes spawned by development tools (VSCode, Cursor) or Node.js that are engaged in suspicious activities, specifically communicating with the Telegram Bot API or uploading large amounts of data, often accompanied by file staging (ZIP archive creation). This behavior is characteristic of the InvisibleFerret information stealer.
Clipboard hijacking malware for cryptocurrency address substitution
Detects the presence and execution of RustDesk remote access software by monitoring process names, command-line arguments, specific destination network ports, and DNS queries associated with the application. This rule identifies potential unauthorized remote access attempts or the use of non-approved remote support tools.
This rule detects network connections from devices to a predefined list of known malicious domains. It identifies attempts by internal systems to communicate with command and control servers, phishing sites, or other infrastructure associated with threat actors.
This rule detects DNS queries for reverse lookups, specifically those ending with '.in-addr.arpa' or '.ip6.arpa'. While legitimate, frequent or unusual reverse DNS queries from a specific device or process could indicate reconnaissance activities or attempts to map internal network infrastructure by an attacker.
Detects high frequency inbound network connection attempts or accepted connections initiated by common file sharing processes (e.g., NearShare.exe, NearbySharing.exe, quickshare.exe, sharingd, airplayd). A high volume of inbound connection attempts may indicate brute force, discovery, or malicious scanning behavior using legitimate file sharing tools as a conduit.
Detects unexpected crashes of critical macOS system processes such as sharingd, nsurlsessiond, airplayd, AirPlayXPCHelper, and NetAuthSysAgent. Frequent crashing of these components can indicate instability or potentially malicious attempts to interfere with system services.
Detects network connection attempts directed toward RFC 1918 private IP address spaces (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16) and localhost (127.0.0.0/8) that do not involve common Windows file sharing ports (TCP/445 and TCP/139). This behavior may indicate lateral movement, internal reconnaissance, or the use of non-standard protocols for internal communication.
Detects abnormal termination, crash, or error messages related to the 'sharingd' process on macOS. 'sharingd' is a daemon responsible for various sharing features including AirDrop, Handoff, and Instant Hotspot. Unexpected errors or crashes within this process may indicate service instability, potential exploitation attempts, or abnormal interactions with sharing services.
Detects the creation of power management assertions using the IOPMAssertionCreateWithName API by processes other than standard system binaries. Attackers may use this technique to prevent a system from sleeping, which can be leveraged to maintain persistence or ensure that malicious background activities continue without interruption.
Detects network connection activity on common C2 ports from processes executing outside of standard system directories. The rule filters out known system paths on macOS and identifies processes that establish multiple connections to non-private IP addresses, assigning a risk score based on the volume of connections and the duration of the activity to identify potential command and control (C2) beaconing behavior.
Detects instances where a Python interpreter (python.exe or python3.exe) initiates an interactive command-line shell (cmd.exe, powershell.exe, bash.exe, or sh.exe). The detection logic further narrows this behavior by looking for common reverse shell indicators such as socket connections or command execution arguments within the process command line.
Detects a high volume of successful network connections to multiple unique cloud storage services (Dropbox, Box.com, OneDrive) from a single device and user account within an hour. This could indicate data exfiltration or unauthorized access to cloud storage.
Page 93 of 173