Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
40,063 detections
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Detects NeedyMantis-style DLL sideloading: a DLL is dropped at an anomalous ProgramData/ProgramFiles path mirroring known first-stage loader naming/path conventions (masquerading as Poedit, curl, Vim, TightVNC, Office, Broadcom, Intel, or NVIDIA components), AND that exact same DLL is subsequently loaded by a process within 10 minutes. Requiring the corroborating load event (rather than file presence alone) filters out benign drops such as installer staging, AV quarantine copies, or backup/sync tools that never execute the file.
This rule detects network connections to known cryptomining pool IP addresses and domain names, as well as file or process executions matching specific hashes associated with known miner samples. It monitors for communication to command-and-control (C2) servers potentially used for cryptomining operations.
This rule detects instances where a Node.js process spawns a child Node.js process from a 'node_modules' directory using configuration flags typically associated with hidden, detached background tasks (e.g., 'detached', 'stdio', 'windowsHide', 'ignore'). It explicitly excludes common Node.js process managers and build tools to reduce false positives associated with legitimate development activities.
Detects the execution of PowerShell or PowerShell ISE where the parent process is the console host (conhost.exe) and the command-line arguments are either missing, empty, or represent an bare execution of the binary. This pattern is commonly associated with fileless execution techniques where attackers attempt to hide command-line arguments or evade logging.
Detects execution patterns indicative of 'ClickFix' social engineering, where a user is tricked into copying a command to their clipboard and executing it directly in a terminal. The rule monitors for processes that read clipboard contents (via PowerShell or clip.exe) and simultaneously invoke shell interpreters to execute the retrieved data.
Detects the deployment of NetSupport Manager (client32.exe) or related components when initiated by common scripting interpreters such as PowerShell, MSHTA, or CMD. This behavior is indicative of a ClickFix-style social engineering attack where a user is coerced into executing malicious commands to deploy remote access tools.
Detects instances where cmd.exe or powershell.exe are launched from explorer.exe with command-line arguments containing DocuSign-themed keywords. This behavior is indicative of a 'ClickFix' phishing attack, where a user is socially engineered to copy and execute a malicious command via the terminal after interacting with a fraudulent DocuSign lookalike page.
Detects the silent installation or execution of Remote Monitoring and Management (RMM) tools when launched by script hosts (wscript.exe, mshta.exe, powershell.exe) which themselves were spawned by browser processes or explorer.exe. This pattern is characteristic of social engineering delivery chains, such as 'ClickFix', where a user is tricked into executing a script that subsequently fetches and runs RMM payloads for persistent remote access.
Detects behavior indicative of the MLTBackdoor malware, specifically the creation or modification of a DLL file (such as *dlp.dll) shortly after or before the creation of an RC4-encrypted 'data.bin' file on the same host. The rule also looks for an optional preceding archive extraction event of common file types from Temp or Downloads directories by standard extraction utilities, which may indicate the staging of the initial payload.
Detects the execution of 'filemanager.exe' and its subsequent access to sensitive browser credential stores (e.g., 'Login Data', 'cookies.sqlite'), occurring within a short window following a PowerShell-based ClickFix-style attack chain. The rule correlates initial suspicious script execution (often involving hidden windows, obfuscated commands, or web requests) with follow-on credential harvesting behavior on the same host.
Detects mshta.exe initiating outbound network connections, followed closely by the execution of a powershell.exe process with an unusually large command line, or PowerShell script block events containing large, potentially obfuscated scripts. This behavior is indicative of 'DeepLoad' or 'ClickFix' style staging, where legitimate binaries are leveraged to download and execute heavily obfuscated payloads.
Detects potential credential theft or malicious browser extension installation (DeepLoad-style) by monitoring for browser extension manifest file drops or registry-based extension force-installs occurring shortly after suspicious PowerShell command execution (ClickFix-style) on the same device.
Detects a sequence of activity where an endpoint performs multiple anti-analysis or sandbox evasion checks (using commands like wmic, powershell, or reg to query system information or look for debugger/VM artifacts) followed within 10 minutes by an outbound network connection to a domain that has not been observed from that specific device in the last 7 days.
Detects potential supply chain attacks where a common external script (e.g., a widget) is loaded across multiple unrelated domains, followed by correlation with suspicious user-executed commands indicative of 'ClickFix' tactics (copying and pasting malicious code from a browser-based overlay into the Windows Run dialog).
Detects a potential ClickFix delivery attack where a user navigates to an automotive-themed website, interacts with the system via the Windows Run dialog (RunMRU), and subsequently executes command-line interpreters (powershell, cmd, wscript, mshta) spawned by explorer.exe within a short time window.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects instances where AI developer tools or IDE assistants (e.g., Claude, Copilot, Cursor) execute data collection commands (such as directory traversal or archiving) followed by or concurrent with the bulk access of sensitive files (credentials, configuration files) or personal user data.
Detects instances where AI developer tools or IDE assistants (e.g., Claude, Copilot, Cursor) execute data collection commands (such as directory traversal or archiving) followed by or concurrent with the bulk access of sensitive files (credentials, configuration files) or personal user data.
Detects instances where AI developer tools or IDE assistants (e.g., Claude, Copilot, Cursor) execute data collection commands (such as directory traversal or archiving) followed by or concurrent with the bulk access of sensitive files (credentials, configuration files) or personal user data.
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