Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
462 detections
Filters
Last updated
All Time
Detection languages
298
91
19
18
17
Contributors
217
125
23
14
13
Categories
198
167
67
55
51
Platforms
363
165
83
10
7
Products / Services
10,665
9,773
6,705
4,421
3,749
462
MITRE Techniques
201
124
113
80
47
CVEs
4
3
3
2
1
The following analytic detects the creation of files in the Windows %startup% folder, a common persistence technique. It leverages the Endpoint.Filesystem data model to identify file creation events in this specific directory. This activity is significant because adversaries often use the startup folder to ensure their malicious code executes automatically upon system boot or user logon. If confirmed malicious, this could allow attackers to maintain persistence on the host, potentially leading to further system compromise and unauthorized access to sensitive information.
The following analytic detects attempts to change the default file association for files without an extension to open with Notepad.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and registry modifications. This activity is significant as it can indicate an attempt to manipulate file handling behavior, a technique observed in APT and ransomware attacks like Prestige. If confirmed malicious, this could allow attackers to execute arbitrary code by tricking users into opening files, potentially leading to system compromise or data exfiltration.
The analytic detects instances where Java or Tomcat processes spawn a Linux shell. This activity may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and parent-child process relationships. This activity is significant as it can signify a compromised Java application, potentially leading to unauthorized shell access. If confirmed malicious, attackers could execute arbitrary commands, escalate privileges, or maintain persistent access, posing a severe threat to the environment.
The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity.
This rule detects the installation or execution of a wide range of common third-party VPN clients by monitoring process creation events. It specifically looks for process names associated with over 100 different VPN services. The rule includes exclusions for legitimate security software like Microsoft Defender and Microsoft Intune Agent, as well as Cisco AnyConnect VPN client installations.
The following analytic detects processes on macOS systems that are tapping keyboard events, potentially monitoring all keystrokes made by a user. It leverages data from osquery results within the Alerts data model, focusing on specific process names and command lines. This activity is significant as it is a common technique used by Remote Access Trojans (RATs) to log keystrokes, posing a serious security risk. If confirmed malicious, this could lead to unauthorized access to sensitive information, including passwords and personal data, compromising the integrity and confidentiality of the system.
The following analytic detects the creation or dropping of executable or script files in the root directory of a removable drive. It leverages data from the Endpoint.Filesystem datamodel, focusing on specific file types and their creation paths. This activity is significant as it may indicate an attempt to spread malware, such as ransomware, via removable media. If confirmed malicious, this behavior could lead to unauthorized code execution, lateral movement, or persistence within the network, potentially compromising sensitive data and systems.
The following analytic detects the loading of drivers from suspicious paths, which is a technique often used by malicious software such as coin miners (e.g., xmrig). It leverages Sysmon EventCode 6 to identify drivers loaded from non-standard directories. This activity is significant because legitimate drivers typically reside in specific system directories, and deviations may indicate malicious activity. If confirmed malicious, this could allow an attacker to execute code at the kernel level, potentially leading to privilege escalation, persistence, or further system compromise.
The analytic detects the creation or enabling of services on Linux platforms using the `systemctl` or `service` tools. It leverages Endpoint Detection and Response (EDR) logs, focusing on process names, parent processes, and command-line executions. This activity is significant as adversaries may create or modify services to maintain persistence or execute malicious payloads. If confirmed malicious, this behavior could lead to persistent access, data theft, ransomware deployment, or other damaging outcomes. Monitoring and investigating such activities are crucial for maintaining the security and integrity of the environment.
Detects .NET DLLs containing hardcoded Azure Storage Account credentials, such as access keys or SAS tokens. This technique was observed in the Axis Communications plugin for Autodesk Revit, where it could have enabled a supply chain attack.
Detects Chaos C++ ransomware payloads based on known file hashes and unique embedded strings from the malware.
Detects attempts to suspend or terminate Endpoint Detection and Response (EDR) agent processes by targeting their Process IDs (PIDs). The rule identifies suspicious command-line arguments and tools (e.g., procdump, WerFault) used to interact with processes, specifically looking for these actions against a predefined list of common EDR agent executable names. It correlates process creation events with process ID resolution to identify the targeted EDR agent.
Detects loaders associated with RainyDay, Turian, and a new PlugX variant, used by threat actors like Naikon and BackdoorDiplomacy. The rule identifies the shared unpacking routine which uses a combination of RC4 decryption and LZNT1 decompression (via RtlDecompressBuffer), along with specific RC4 keys, PDB paths, and hardcoded payload filenames.
Detects a Windows command shell executing a batch file that subsequently spawns processes for system discovery (often for AV checking) and then either executes a potential payload or performs network reconnaissance. This behavior is consistent with the initial stages of the BlockBlasters malware campaign.
The analytic detects the writing of files from known remote access software to disk within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on file path, file name, and user information. This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration, further compromise, or complete control over affected systems.
The following analytic detects the use of the EncodedCommand parameter in PowerShell processes. It leverages Endpoint Detection and Response (EDR) data to identify variations of the EncodedCommand parameter, including shortened forms and different command switch types. This activity is significant because adversaries often use encoded commands to obfuscate malicious scripts, making detection harder. If confirmed malicious, this behavior could allow attackers to execute hidden code, potentially leading to unauthorized access, privilege escalation, or persistent threats within the environment. Review parallel events to determine legitimacy and tune based on known administrative scripts.
Detects the creation of a mutex with a pattern matching new SmokeLoader versions (2025 alpha and 2025). The malware's main module is injected into explorer.exe, which then creates this distinct mutex to prevent multiple instances from running.
Detects attempts to add sensitive privileges to a process token, a common technique for local privilege escalation. This could be part of an exploit chain following an information disclosure vulnerability like CVE-2025-53136.
The analytic detects outbound network connections from an endpoint process to suspicious or non-standard ports. It uses Cisco Network Visibility Module flow data to identify processes communicating over ports like 4444, 2222, or 51820, which are commonly associated with tools such as Metasploit, SliverC2, other penetration testing tools, red team activities, or malware. These connections warrant further investigation, especially when initiated by unexpected or non-network-native binaries.
The analytic detects the creation of an Outlook Macro (VbaProject.OTM) by a suspicious process. This file is normally created when you create a macro from within Outlook. If this file is created by a process other than Outlook.exe it may be maliciously created. This detection leverages data from the Filesystem datamodel, specifically looking for the file creation event for VbaProject.OTM. This activity is significant as it is commonly associated with some malware infections, indicating potential malicious intent to harvest email information.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
Page 13 of 22