Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
20,480 detections
Filters
Last updated
All Time
Detection languages
8,114
7,158
1,351
1,166
1,063
Contributors
4,878
2,724
2,695
2,646
2,356
Categories
20,480
11,864
5,884
5,164
4,867
Platforms
18,083
3,046
1,506
124
104
Products / Services
5,723
4,293
3,713
1,173
958
MITRE Techniques
9,480
7,573
3,458
2,848
2,702
CVEs
36
28
25
21
20
IDS Classtypes
1
The following analytic detects the execution of `wmic.exe` with the `node` switch, indicating an attempt to spawn a local or remote process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, the attacker could gain remote control over the targeted system, execute arbitrary commands, and potentially escalate privileges or persist within the environment.
The following analytic detects the execution of sdbinst.exe with parameters indicative of silently creating a shim database. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line arguments. This activity is significant because shim databases can be used to intercept and manipulate API calls, potentially allowing attackers to bypass security controls or achieve persistence. If confirmed malicious, this could enable unauthorized code execution, privilege escalation, or persistent access to the compromised system.
The following analytic identifies child processes spawned from "mshta.exe". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific child processes like "powershell.exe" and "cmd.exe". This activity is significant because "mshta.exe" is often exploited by attackers to execute malicious scripts or commands. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. Monitoring this activity helps in early detection of potential threats leveraging "mshta.exe" for malicious purposes.
The following analytic identifies instances of reg.exe being launched from a command prompt (cmd.exe) that was not initiated by the user, as indicated by a parent process other than explorer.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names. This activity is significant because reg.exe is often used in registry manipulation, which can be indicative of malicious behavior such as persistence mechanisms or system configuration changes. If confirmed malicious, this could allow an attacker to modify critical system settings, potentially leading to privilege escalation or persistent access.
The following analytic detects the creation of a local administrator account using the "net.exe" command. It leverages Endpoint Detection and Response (EDR) data to identify processes named "net.exe" with the "/add" parameter and keywords related to administrator accounts. This activity is significant as it may indicate an attacker attempting to gain persistent access or escalate privileges. If confirmed malicious, this could lead to unauthorized access, data theft, or further system compromise. Review the process details, user context, and related artifacts to determine the legitimacy of the activity.
The following analytic detects the use of AppCmd.exe to disable HTTP logging on IIS servers. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution events where AppCmd.exe is used with specific parameters to alter logging settings. This activity is significant because disabling HTTP logging can help adversaries hide their tracks and avoid detection by removing evidence of their actions. If confirmed malicious, this could allow attackers to operate undetected, making it difficult to trace their activities and respond to the intrusion effectively.
The following analytic detects changes in the ScManager service security descriptor settings. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for any process execution involving the "sc.exe" binary with the "sdset" flag targeting the "scmanager" service. If confirmed malicious, this could allow an attacker to escalate their privileges.
The following analytic detects the execution of the Windows OS tool quser.exe, commonly used to gather information about user sessions on a Remote Desktop Session Host server. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial as quser.exe is often abused by post-exploitation tools like winpeas, used in ransomware attacks to enumerate user sessions. If confirmed malicious, attackers could leverage this information to further compromise the system, maintain persistence, or escalate privileges.
This analytic detects Windows processes such as cmd.exe, PowerShell, and common Windows LOLBINs being spawned by React or Next.js application servers.
In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands.
This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process.
Such activity might be a strong indicator of exploitation of the aforementioned vulnerability.
In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands.
This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process.
Such activity might be a strong indicator of exploitation of the aforementioned vulnerability.
Hunts for sqlservr.exe spawning command shells or system utilities, a pattern consistent with xp_cmdshell abuse. It also flags later steps from a public intrusion write-up: certutil decoding, SAM, SYSTEM or SECURITY hive saves and local account or group changes.
Results are grouped per device and day and ranked by stage count. Rows can also appear without a SQL parent when two later stages occur on the same device and day.
Triage: Some jobs use xp_cmdshell legitimately. Review the command line and the account. Hunting query, not an alert.
Limitations: Activity inside the SQL process with no child process is not visible. Renamed tools, obfuscation and privilege escalation tools are not covered. Needs Defender for Endpoint on the SQL servers.
Results are grouped per device and day and ranked by stage count. Rows can also appear without a SQL parent when two later stages occur on the same device and day.
Triage: Some jobs use xp_cmdshell legitimately. Review the command line and the account. Hunting query, not an alert.
Limitations: Activity inside the SQL process with no child process is not visible. Renamed tools, obfuscation and privilege escalation tools are not covered. Needs Defender for Endpoint on the SQL servers.
Detects a process other than XBootMgr.exe spawned by XBootMgrSleep.exe.
XBootMgrSleep.exe is a Microsoft-signed Windows Performance Toolkit binary that can execute an arbitrary executable after a delay.
XBootMgrSleep.exe is a Microsoft-signed Windows Performance Toolkit binary that can execute an arbitrary executable after a delay.
The following analytic detects the spawning of a PowerShell process as a child or grandchild of commonly abused processes like services.exe, wmiprvse.exe, svchost.exe, wsmprovhost.exe, and mmc.exe.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names, as well as command-line executions.
This activity is significant as it could indicates lateral movement or remote code execution attempts by adversaries.
If confirmed malicious, this behavior could allow attackers to execute code remotely, escalate privileges, or persist within the environment.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names, as well as command-line executions.
This activity is significant as it could indicates lateral movement or remote code execution attempts by adversaries.
If confirmed malicious, this behavior could allow attackers to execute code remotely, escalate privileges, or persist within the environment.
This detection flags a parent and child process pair on internet-facing Windows servers, where the parent is a web server process and the child is the process it launched. The web server processes covered are w3wp, httpd, nginx and Tomcat, including versioned Tomcat service executables such as tomcat9.exe and Tomcat running under java.exe, which is identified by the Catalina bootstrap class in its command line. A server is treated as internet-facing based on the most recent status recorded for it.
Instead of matching known web shell names, the query builds a baseline from the previous 30 days of process activity on each device and alerts when a pair appears in the latest window that has never been seen on that device before, so it can catch web shell activity it has never seen before. Because the baseline is held per device, a pair that is normal on one server does not hide the same pair appearing on another.
The rule runs every 24 hours, as this is the only Defender custom detection frequency with a 30-day lookback, and the alert window covers the last 25 hours so that a slightly delayed run does not leave a gap.
The baseline and alert windows are separated by ingestion time rather than event time, so events that arrive late are still evaluated.
Instead of matching known web shell names, the query builds a baseline from the previous 30 days of process activity on each device and alerts when a pair appears in the latest window that has never been seen on that device before, so it can catch web shell activity it has never seen before. Because the baseline is held per device, a pair that is normal on one server does not hide the same pair appearing on another.
The rule runs every 24 hours, as this is the only Defender custom detection frequency with a 30-day lookback, and the alert window covers the last 25 hours so that a slightly delayed run does not leave a gap.
The baseline and alert windows are separated by ingestion time rather than event time, so events that arrive late are still evaluated.
Detects a behavioral chain where PowerShell stages a hidden base64-encoded command and schedules a VBScript task, or PowerShell fetches an upgrade.txt file via web client methods for execution.
Detects the usage of command-line flags in AI coding assistant CLIs (such as Claude Code, Amazon Q CLI, and others) that intentionally skip security permissions, bypass approvals, or disable sandbox protections. The rule identifies potential developer activity that may inadvertently reduce the security posture of the development environment.
Detects curl.exe writing an MSI package into the ProgramData
directory. Chaos ransomware stages a trojanized installer this way, fetching
update_ms.msi from an attacker-controlled host over plain HTTP on port 443 so
the traffic reads as TLS to port-based inspection. The match is on the
behaviour rather than on that filename: curl is a signed in-box binary and has
no legitimate reason to drop an installer package into ProgramData.
directory. Chaos ransomware stages a trojanized installer this way, fetching
update_ms.msi from an attacker-controlled host over plain HTTP on port 443 so
the traffic reads as TLS to port-based inspection. The match is on the
behaviour rather than on that filename: curl is a signed in-box binary and has
no legitimate reason to drop an installer package into ProgramData.
Detects cmd.exe spawned by a python.exe or pip.exe process with a command line invoking netstat or calc.exe. Matches the payload behavior described for the malicious redpy_demo PyPI package, whose __main__.py used Python's subprocess module to execute these commands via cmd.exe automatically at module invocation, without any direct victim interaction with the payload.
Detects a setx.exe process launched by python.exe or pip.exe with a command line referencing the PYTHONPATH environment variable. This matches setup.py build-time tampering that persistently rewrites the user's PYTHONPATH, redirecting future module resolution for the affected user to an attacker-controlled directory across all subsequent Python invocations.
Detects remote service creation where the service binary is launched from an SMB administrative share (ADMIN$ or C$), indicating potential PsExec-style lateral movement.
Detects an AhsayCBS / Tomcat / Java web service process spawning a
command or script interpreter. After exploiting AhsayCBS, attackers executed
shell commands through the dropped webshell to run reconnaissance and download
the XMRig payload. A backup web service should never directly spawn cmd.exe,
powershell.exe, or a Unix shell - this indicates webshell command execution.
command or script interpreter. After exploiting AhsayCBS, attackers executed
shell commands through the dropped webshell to run reconnaissance and download
the XMRig payload. A backup web service should never directly spawn cmd.exe,
powershell.exe, or a Unix shell - this indicates webshell command execution.
The following analytic identifies the use of a `for /f` loop with the `delims=` option within `cmd.exe`, a technique commonly used to parse and extract data from the output of other commands.
Adversaries and malicious scripts leverage this pattern to programmatically process command output for discovery, data extraction, or execution purposes while evading simpler detection logic.
Adversaries and malicious scripts leverage this pattern to programmatically process command output for discovery, data extraction, or execution purposes while evading simpler detection logic.
Page 19 of 976