Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potentially malicious child process creation or unauthorized outbound network connections initiated by AI-assisted coding tools (e.g., Claude, Cursor, Windsurf) through their integrated Node.js or Python environments. This rule identifies common post-exploitation behaviors such as shell execution, command-line reconnaissance, and connections to external non-internal IP addresses.
This rule detects outbound network connections to a specific typosquatted domain 'perplexity-ai.online'. This domain is associated with a malicious browser extension designed to intercept and exfiltrate search queries before forwarding them to legitimate search providers.
Detects the creation or modification of an mcp.json file within IDE configuration directories (.vscode, .amazonq), followed shortly by the execution of shell or interpreter processes (bash, sh, cmd, powershell, npx) by the IDE process. This behavior suggests the installation of a malicious Model Context Protocol (MCP) server configuration that could facilitate command execution or persistence within the developer environment.
Detects suspicious child process activity originating from common IDEs or AI language server processes that indicates AWS credential exfiltration. The rule monitors for command-line patterns indicative of credential dumping or exfiltration, such as the use of AWS CLI commands, curl, or npx with the Model Context Protocol (MCP) in conjunction with attempts to access sensitive environment variables or redirect output.
This rule detects potentially malicious child processes spawned by Amazon Q or VS Code language server processes. It monitors for suspicious shell command-line invocations (e.g., bash, sh, cmd.exe) or tools (npx, python) immediately following the activation of language server processes, specifically looking for patterns associated with the unauthorized auto-loading or execution of Model Context Protocol (MCP) configuration files or utilities, potentially related to vulnerability patterns involving workspace-based execution.
This rule detects network activity and email signals associated with a malicious campaign promoting a fraudulent 'Perplexity AI' Chrome extension. It monitors for connections to known malicious Tilda landing pages, correlates these visits with subsequent Google Chrome extension (CRX) downloads within a 10-minute window, and identifies phishing emails containing links to these malicious pages.
Detects the Ghidra reverse engineering suite spawning suspicious child processes such as command shells, scripting interpreters, or network utilities. This activity often indicates an attempt to automate reverse engineering tasks or interact with the system in an unintended manner by potentially malicious scripts or plugins.
Detects potentially malicious activity associated with the Ghidra reverse engineering framework, including the spawning of unexpected non-Java child processes, loading of suspicious project files (.gzf/.gar), loading of unsigned extensions from non-standard directories, and unexpected outbound network connections from decompiler or Java processes.
Detects unauthorized processes (Python, Node.js) accessing sensitive files such as browser login data, cookie stores, wallet files, and SSH/AWS keys, which are common indicators of credential theft and data exfiltration.
Detects rapid post-exploitation activity where a developer's workstation, upon executing VS Code (Code.exe), initiates outbound network connections to major cloud service or CI/CD provider APIs (GitHub, AWS, Azure, GCP) within 60 minutes. This behavior is indicative of the Lazarus Group 'Contagious Interview' campaign, where stolen credentials from developer environments are used to pivot into cloud infrastructure or CI/CD pipelines.
This rule monitors process creation events for 'git' or 'npm' command-line activities that are highly indicative of potential software supply chain attacks. It triggers when known malicious package names or suspicious patterns are detected within command-line arguments, or when a workflow involves a git clone followed by subsequent npm installation, which is a common vector for deploying malicious dependencies.
Detects instances where Python or Node.js processes access common cloud provider credential files (e.g., AWS, GCP, Azure). This behavior is often associated with infostealer campaigns, such as the 'Contagious Interview' campaign, where adversaries attempt to harvest stored cloud credentials for further compromise.
Detects instances where scripting or development processes (e.g., node.exe, python.exe, code.exe) access sensitive SSH private key files or PEM files, followed by an immediate outbound network connection to a non-private IP address within 60 seconds. This activity is indicative of credential exfiltration or unauthorized use of stolen keys.
This rule monitors DNS query events for domains known to be associated with phishing campaigns, specifically those spoofing Microsoft or other common service brands. These domains are frequently used in credential harvesting attacks to lure users to malicious landing pages.
Detects network activity and command line execution associated with known malicious browser extensions, specifically targeting AdBlock for Youtube-related extensions and associated command-and-control infrastructure.
Detects Python or Node.js processes executing command-line interpreters (e.g., cmd.exe, bash) while simultaneously performing network connections. This behavior is indicative of a post-exploitation activity where an attacker leverages language-specific package paths to execute shell commands and establish command-and-control communication.
Detects unauthorized processes reading AI assistant configuration files (e.g., Claude, Cursor, Gemini, VS Code) followed by an outbound network connection from a common scripting or runtime environment (node, python, bun) within 60 seconds, potentially indicating credential or configuration exfiltration.
This rule monitors for suspicious child processes (such as bash, curl, or aws cli) spawned by the Amazon Q IDE language server. These patterns are indicative of potential abuse of IDE plugins or language servers to execute system commands, exfiltrate credentials, or interact with cloud APIs, potentially utilizing custom MCP (Model Context Protocol) configurations.
Detects unauthorized or suspicious AWS CLI operations and sensitive IAM API calls initiated from developer tooling processes like VS Code (including the Amazon Q extension host). This behavior is often associated with the exploitation of MCP (Model Context Protocol) auto-execution vulnerabilities, where attackers attempt to use compromised developer environments to escalate privileges or exfiltrate cloud credentials.
Detects unexpected subprocesses (bash, sh, npx) spawned by the Amazon Q language server process. This behavior is indicative of a potential exploit targeting the Model Context Protocol (MCP) auto-execution vulnerability (tracked as CVE-2026-12957), where a malicious .amazonq/mcp.json workspace configuration is used to gain unauthorized code execution.
Detects Node.js executing the malicious 'setup.cjs' script with the '--no-warnings' flag. This execution sequence is associated with the 'easy-day-js' typosquat postinstall hook used in the Mastra npm supply chain compromise.
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