Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous PowerShell script execution originating from GitLab CI runners where the environment variables suggest unauthorized command execution, specifically identifying usage of 'IEX' to execute commands passed via the 'CMD' environment variable, often used in GitRunner C2 techniques.
Detects instances where the Greenshot.exe application loads DLLs (log4net.dll, GreenshotPlugin.dll, LinqBridge.dll) from locations outside of the expected installation directory ('\Program Files\Greenshot' or '\Program Files (x86)\Greenshot'). The rule also tracks the creation of configuration or updater files ('greenshot-defaults.ini', 'updater.bat') which may be indicative of unauthorized modification, persistence, or potential DLL side-loading attempts.
Detects rapid execution of multiple discovery-related commands (whoami, tasklist, ipconfig, netstat, systeminfo) by common Windows host processes within a 15-minute window. This behavior often indicates an adversary performing reconnaissance after gaining an initial foothold.
This rule detects the loading of a driver named 'eb.sys' (or matching a specific SHA256 hash) paired with the creation of a Windows service for that driver via the 'sc' command. This behavior is indicative of potential rootkit installation or driver-based persistence mechanisms.
Detects execution of Windows Installer packages (MSI) masquerading as common software installers (Spotify, Zoom, Microsoft Teams) using known ChainScript-style naming patterns and suspicious command-line parameters (e.g., ALLUSERS=2, MSIINSTALLPERUSER=1) or invocation from common user-mode applications like cmd.exe, powershell.exe, or explorer.exe.
Detects the execution of processes or creation of events attempting to mimic Windows Security credential prompts. This pattern is characteristic of the TukTuk C2 framework's 'Cred Prompt' feature, designed to harvest user credentials by presenting a fake authentication dialog.
Detects the use of PowerShell to delete files with common script or shortcut extensions from directories frequently used for persistence or staging malicious payloads, such as APPDATA, ProgramData, and the Startup folder.
Detects the execution of PowerShell scripts that use Invoke-Expression and Invoke-RestMethod in combination with specific suspicious domains, file extensions, or indicators of web-based payload retrieval. This pattern is commonly used for staging and executing malicious payloads directly from the internet.
Detects instances where a process directly reads ntdll.dll from the disk (e.g., C:\Windows\System32). This behavior is a common technique used by malware to bypass EDR hooks by reading a clean copy of the DLL to restore original syscall stubs, commonly known as hook removal or unhooking. The rule excludes common system processes and trusted security software paths.
Detects instances where a process directly reads ntdll.dll from the disk (e.g., C:\Windows\System32). This behavior is a common technique used by malware to bypass EDR hooks by reading a clean copy of the DLL to restore original syscall stubs, commonly known as hook removal or unhooking. The rule excludes common system processes and trusted security software paths.
Detects unauthorized code injection attempts into web browser processes (chrome.exe, msedge.exe) by non-browser related processes. This behavior involves using APIs like CreateRemoteThread, NtAllocateVirtualMemory, or NtWriteVirtualMemory to modify memory and execute remote threads, which is a common technique used by information stealers such as LummaC2 to bypass browser security controls.
This rule monitors for suspicious activity related to Component Object Model (COM) objects, including registry modifications (creation, setting, or checking of specific CLSIDs related to shell links or WMI locators) in conjunction with process-level alerts (such as Remote Thread API calls, primary token modification, or WMI activity). It specifically filters out known benign system processes to focus on anomalous behavior that may indicate COM hijacking, persistence, or lateral movement.
Detects a suspicious execution chain where a browser process makes a network connection, followed shortly by a direct execution of command-line tools (powershell.exe, mshta.exe, or cmd.exe) from the Windows explorer.exe process (e.g., via the Run dialog). This behavior is characteristic of 'ClickFix' social engineering attacks where users are tricked into copying and pasting malicious commands from a web page directly into their local environment.
Detects the use of the 'finger.exe' utility in a suspicious manner, indicative of payload staging in a 'ClickFix' phishing attack scenario. The rule monitors for instances where 'finger.exe' is executed with suspicious command-line arguments (containing '@' or '-l') by common parent processes associated with malicious copy-paste activities (cmd.exe, powershell.exe, or explorer.exe).
Detects ClickFix-style browser or explorer spawned command execution using clipboard-paste patterns, or subsequent ransomware-related discovery and inhibit-system-recovery commands such as net.exe, nltest.exe, and vssadmin.exe used after initial access.
Detects a multi-stage Telephone-Oriented Attack Delivery (TOAD) campaign. The first stage identifies phishing emails containing billing or security-themed subjects and phone numbers without links or attachments to evade automated analysis. The second stage monitors for the subsequent installation or execution of common remote-support tools (e.g., AnyDesk, TeamViewer, ScreenConnect). A final correlative rule detects these events occurring sequentially on the same host, indicating a high-confidence indicator of a successful callback phishing social-engineering attempt.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
Detects a two-stage activity: first, the potential unauthorized reading or access of LLM provider API keys from files, environment variables, or command-line arguments; and second, the subsequent use of those same credentials to authenticate to LLM provider APIs (OpenAI, Anthropic, Azure, Bedrock) from a different, unrecognized source IP address, indicating potential credential theft and session hijacking.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
Detects a two-stage activity: first, the potential unauthorized reading or access of LLM provider API keys from files, environment variables, or command-line arguments; and second, the subsequent use of those same credentials to authenticate to LLM provider APIs (OpenAI, Anthropic, Azure, Bedrock) from a different, unrecognized source IP address, indicating potential credential theft and session hijacking.
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.
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