Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
224 detections
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KQL detects suspicious child processes launched by PHP or web-server workers following possible exploitation of CVE-2026-109618 or CVE-2026-109619.
Detects Sysinternals executables written to disk on Windows endpoints, identified by the Sysinternals company name in the PE metadata of newly written EXE files. These are legitimate admin utilities, but attackers also copy them to hosts for discovery, process inspection and credential access. The rule shows tool presence only, not misuse, so it suits inventory and hunting. To triage, review the file path, hash, writing process and whether the user is an administrator.
Detects web server processes (such as nginx, apache2, httpd, uwsgi, gunicorn, php-fpm) spawning command-line utilities typically used for system and user discovery (such as id, whoami, groups, hostname, uname, netstat, ps, etc.), which often indicates web application exploitation and remote code execution.
This rule detects suspicious activity where a user account executes multiple disparate reconnaissance or discovery commands within a 30-minute window. It monitors for the execution of common Windows administration and discovery tools like dsquery, nltest, net, nslookup, and tasklist using specific, non-standard arguments indicative of network or domain reconnaissance.
Detects HTTP traffic associated with the NeedyMantis malware, specifically identifying initial beacons to a known C2 domain using a specific User-Agent, followed by command-and-control response headers that suggest the exfiltration of reconnaissance data via the Set-Cookie field.
Detects a suspicious sequence of command execution patterns characteristic of the Kothamine agent performing post-compromise system discovery. The rule triggers when multiple discovery commands (tasklist, ipconfig, taskkill) are executed by the same process instance within a 5-minute window, consistent with typical C2 behavior for reconnaissance.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects host-based reconnaissance activity involving system, network, and security software enumeration using standard administrative tools (e.g., PowerShell, WMI, netsh) that is followed by an outbound network connection to a known or suspicious implant check-in endpoint within a 30-minute window.
Detects reconnaissance and situational-awareness collection commands (e.g., tasklist, systeminfo, wmic, powershell) executed by processes matching suspected persistence artifacts associated with known malicious activity (specifically referencing potential Telegram C2 tasking). The rule monitors common discovery utilities spawned by suspicious process names or paths.
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects a suspicious sequence of command execution patterns characteristic of the Kothamine agent performing post-compromise system discovery. The rule triggers when multiple discovery commands (tasklist, ipconfig, taskkill) are executed by the same process instance within a 5-minute window, consistent with typical C2 behavior for reconnaissance.
Detects a suspicious sequence of command execution patterns characteristic of the Kothamine agent performing post-compromise system discovery. The rule triggers when multiple discovery commands (tasklist, ipconfig, taskkill) are executed by the same process instance within a 5-minute window, consistent with typical C2 behavior for reconnaissance.
Detects a suspicious sequence of command execution patterns characteristic of the Kothamine agent performing post-compromise system discovery. The rule triggers when multiple discovery commands (tasklist, ipconfig, taskkill) are executed by the same process instance within a 5-minute window, consistent with typical C2 behavior for reconnaissance.
Detects a suspicious sequence of command execution patterns characteristic of the Kothamine agent performing post-compromise system discovery. The rule triggers when multiple discovery commands (tasklist, ipconfig, taskkill) are executed by the same process instance within a 5-minute window, consistent with typical C2 behavior for reconnaissance.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects the execution of 'userscreen.exe' (a renamed pythonw.exe used by NarwhalRAT) loading 'user32.dll'. This behavior is associated with the use of ctypes in Python to perform window enumeration via EnumWindows, specifically used by the malware to identify and monitor KakaoTalk application windows.
Detects Node.js processes, such as npm package postinstall hooks, spawning shell commands used for host enumeration. This includes accessing system environment variables, network configurations, and filesystem metadata, a pattern frequently utilized by malicious packages for initial reconnaissance post-installation.
Detects a sequence of distinct system and network reconnaissance commands typically performed by malware or RATs for environment fingerprinting. The rule triggers when three or more unique reconnaissance activities—such as querying antivirus status, enumerating network adapters, checking domain information, or listing installed software—occur from the same host within a 5-minute window.
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
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