Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the use of AWS CloudShell to execute commands that download or install external files (e.g., using curl, wget, pip, or git), excluding those originating from known approved sources such as AWS-owned domains or standard package registries. This behavior may indicate an adversary attempting to download offensive tooling or backdoors into the cloud environment.
Detects a multi-stage loader execution chain, potentially named GHAPPIER, by identifying sequential stages of execution. These stages include initial marker file checks, shell-based command dispatching via curl/wget, renaming and execution of secondary payloads, Node.js-based runtime staging, and the eventual creation of implant artifacts in common paths such as VSCode extensions or hidden directories.
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 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 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 detects artifacts and behaviors associated with the WaterPlum/Contagious Interview (also known as DeceptiveDevelopment) malware suite, including BeaverTail, InvisibleFerret, and related payloads. The rule specifically targets the presence of embedded family-name strings, as well as the execution of suspicious npm package installations triggered by VS Code task automation (tasks.json) or trust prompts, common in fake technical interview attack scenarios.
The following analytic detects when all virtual machines on an ESXi host are abruptly terminated, which may indicate malicious activity such as a deliberate denial-of-service, ransomware staging, or an attempt to destroy critical workloads.
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 evidence of PowerShell execution occurring within a GitLab CI runner environment that exhibits characteristics of command-and-control (C2) activity. This rule monitors PowerShell Script Block Logging (Event ID 4104) for indicators such as specific CI environment variables (e.g., CI_PIPELINE_SOURCE=api, GIT_STRATEGY=none), static job names associated with known exploitation patterns (e.g., CI_JOB_NAME=run_command), and the dynamic execution of commands using Invoke-Expression (IEX) on environmental variables.
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 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 Command and Control (C2) activity leveraging the OpenAI Assistants API. The rule identifies non-standard processes (e.g., PowerShell, Python, cmd) that establish network connections to OpenAI API endpoints, specifically those often used for interaction with AI models, suggesting potential abuse of AI services for command issuance or data exfiltration.
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 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.
Detects the WMI provider host (wmiprvse.exe) spawning common living-off-the-land binaries often associated with command execution or lateral movement.
Detects the creation of scheduled tasks using the 'schtasks.exe' utility where the command line contains suspicious patterns such as execution of common scripting engines (PowerShell, wscript, cscript), use of common staging directories (temp, appdata, programdata), or naming conventions that mimic legitimate Windows system tasks.
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