Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
Detects execution of rundll32.exe with suspicious command-line patterns, including paths containing 'DavWWWRoot' (indicating potential remote file execution/WebDAV abuse), specific malicious DLL function exports, or launching from PowerShell. These techniques are commonly used to execute payloads from remote locations or to bypass traditional security controls.
Detects instances where a Node.js process (node.exe) initiates command-line interpreters (cmd.exe, powershell.exe) with specific arguments or command lines associated with administrative or potentially obfuscated/automated script execution. This is a common pattern for post-exploitation activities or legitimate administrative automation.
This rule detects a sequential multi-stage execution chain starting with msiexec.exe launching a hidden PowerShell script, followed by wscript.exe executing a VBScript agent, and finally node.exe executing a JavaScript file within a short time window. This pattern is indicative of a complex, multi-stage dropper or malware execution flow.
Detects the presence of Node.js runtime files (node.exe, index.js) or script-based agents (vbs, ps1) located within user-profile paths (AppData) that mimic legitimate Windows system folders. This pattern is characteristic of masquerading techniques used to stage or run malicious agents under the guise of system components.
Detects instances where the Node.js runtime process (node.exe) spawns a command shell (cmd.exe) or PowerShell (powershell.exe). This pattern is frequently used by malicious Node.js-based applications, RATs, or web shells to execute arbitrary commands on a compromised host.
Detects a suspected social engineering attack chain where a user communicates with a recruiter via messaging apps or browsers, subsequently clones or installs a malicious repository using 'npm' or 'VS Code', and triggers suspicious child process activity from 'node.exe'. This pattern is associated with the WaterPlum campaign.
Detects Node.js or Python processes exhibiting behavior characteristic of information-stealing malware (such as the WaterPlum suite: BeaverTail, InvisibleFerret, OtterCookie, StoatWaffle). The rule monitors for these processes accessing sensitive files related to browser credentials, cryptocurrency wallets, scanned identification documents, or performing collection activities like keylogging and screen capturing.
Detects suspicious command-line patterns typically used for downloading or executing malicious payloads, such as curl, base64 encoding, and PowerShell web requests, when spawned directly from Node.js or Visual Studio Code processes. This behavior is associated with supply chain compromises and loader activity (e.g., WaterPlum/Contagious Interview) targeting development environments.
This rule monitors for the installation of specific known-malicious or suspicious NPM packages that are commonly used in supply chain attacks. It identifies the execution of npm (or node) commands to install or add packages explicitly listed as malicious by security research, which may indicate a supply chain compromise or an attempt to introduce unauthorized code into the development environment.
Detects the use of 7-Zip (7z.exe) to extract a file named drata.dat using command line arguments that indicate silent extraction from an encrypted or password-protected archive. This behavior is associated with the LegionLoader malware, which uses this technique to stage its payload.
Detects the execution of PowerShell via a shortcut (.lnk) file that attempts to download and execute remote content using 'Invoke-WebRequest'. The command lines use common obfuscation techniques like 'Hidden' window style and 'Bypass' execution policy, targeting specific known malicious URL patterns or filename keywords.
Detects the execution of cloud CLI tools (aws, az, gcloud) using Unix shells (bash, sh) to perform sensitive operations such as listing secrets, keys, or metadata, which may indicate credential discovery or exfiltration.
Detects the use of 'winget configure' to retrieve and apply a Desired State Configuration (DSC) file from a remote HTTPS URL. This behavior can be abused to execute malicious configuration scripts directly from the internet.
Detects the WinGet DSC configuration engine spawning service management processes such as sc.exe or PowerShell to create or modify Windows services. This activity is consistent with the abuse of DSC 'Service' resources to achieve persistence or privilege escalation by registering services to run as LocalSystem.
Detects the use of Windows command shell variable substring expansion to obfuscate commands. Adversaries use this technique to build malicious command lines dynamically, making them less visible to standard keyword-based detection mechanisms by storing parts of strings in environment variables and reassembling them using substring notation (e.g., %var:~start,length%).
Detects potential misuse of AI agentic frameworks (e.g., Semantic Kernel) where the host process executes destructive commands (e.g., file deletion, wiping, shadow copy deletion) or performs large-scale file deletion, which may indicate unauthorized tool invocation or compromised AI agents.
Detects a potential AI agent data poisoning event where a file is downloaded from an untrusted source (browser or sync client) and subsequently read within one hour by a process associated with AI agent activity (e.g., semantic_kernel, SKAgent). This pattern is often used to inject malicious context or prompt injection vectors into RAG (Retrieval-Augmented Generation) systems.
Detects anomalous process execution originating from a Python-based sandbox (such as Semantic Kernel SessionsPythonPlugin in Azure Container Apps) that attempts to break out of the container isolation. The rule monitors for command-line arguments involving interaction with the Docker socket, sensitive host namespaces, or container privilege escalation techniques, indicative of exploitation attempts related to CVE-2026-25592.
Detects instances where AI agent host processes (such as Python, .NET, or IIS worker processes) spawn suspicious child processes like command interpreters or common system utilities. This behavior is indicative of potential Remote Code Execution (RCE) via prompt injection, where an AI agent is manipulated into executing arbitrary system commands.
Detects persistence and execution abuse within hidden IDE/AI assistant workspace directories (e.g., .vscode, .cursor, .claude). The rule monitors for the creation or modification of configuration files used for automated task execution and identifies process execution patterns originating from these directories that invoke downloaders (curl, wget) or decoders (base64) to fetch or execute payloads.
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