Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects rapid access (three or more distinct files within a 60-second window) to sensitive files typically containing credentials or configuration data (e.g., SSH keys, AWS credentials, environment files, container configurations). This pattern is often associated with credential dumping or reconnaissance activity by an adversary attempting to harvest secrets.
This rule detects HTTP/1.1 requests containing an Upgrade header (specifically targeting WebSocket upgrades) accompanied by a non-zero Content-Length header. This pattern is characteristic of a request smuggling primitive targeting the nghttpx reverse proxy, where the provided body may be misinterpreted by the backend as a separate, smuggled request due to improper request queue handling.
Detects a suspicious sequence of events involving the creation of an .amazonq/mcp.json file shortly after a git clone operation, followed by an AI coding assistant (Amazon Q, VS Code, Cursor, Windsurf) spawning runtime interpreters (node, npx, python). This behavior aligns with potential exploitation attempts against AI-assisted development environments, such as CVE-2026-12957, where malicious configuration files are used to trigger unauthorized code execution.
Detects the exploitation of CVE-2026-20253, where the Flowise application fails to correctly sanitize or validate environment variables. By using a case-insensitive bypass (e.g., lowercase 'node_options'), an attacker can cause a spawned Node.js child process to honor the variable and execute unauthorized JavaScript files via the '--require' command-line argument.
Detects the spawning of potential Model Context Protocol (MCP) server processes immediately following or by package installation commands (npm, pip). This behavior is characteristic of malicious packages or typosquatted dependencies attempting to register or execute a malicious MCP server configuration upon installation in a developer environment.
This rule monitors for file system activity associated with the 'libpwn' exploit suite, specifically targeting files related to the exploitation of CVE-2026-55200. It flags the presence of exploit harnesses, probes, and proofs of concept (PoCs) identified by specific filenames and directory structures.
Detects execution of Node.js processes associated with the TaskWeaver JavaScript loader. The rule monitors for unauthorized Node.js processes (lacking official publisher signatures) spawning secondary processes or executing specific suspicious command lines linked to TaskWeaver and Djinn Stealer activity, such as loading obfuscated jquery.js or accessing configuration files associated with credential theft.
This rule detects indicators of activity associated with a Firefox smart_window PoC, specifically designed to exfiltrate private browsing URLs. It monitors for suspicious network connections from the Firefox process to port 8765, the creation of PoC-related scripts, and the execution of associated Node.js processes or command lines.
Detects a potential exploitation of CVE-2026-12957, where a malicious git repository contains an MCP (Model Context Protocol) configuration file. When this workspace is opened, the Amazon Q Developer extension for VS Code improperly processes this configuration, resulting in the unauthorized execution of shell processes (cmd.exe, powershell.exe, bash, or sh) by the extension's host processes (code.exe or node.exe).
Detects artifacts, execution of harness tooling, and post-exploitation behavior associated with CVE-2026-55200 in libssh2. The rule monitors for the presence of known exploit filenames (libpwn), suspicious Python script execution, unexpected shell spawning from SSH-enabled clients, and outbound network connections to non-standard ports typical of exploit scaffolding.
Detects instances where AI coding agents, specifically Claude or Node-based environments, initiate secondary processes like git clone, package installation (pip, npm), or script execution. This behavior often suggests the agent is autonomously pulling and executing code from external repositories without explicit human approval, posing a significant risk of supply chain compromise.
Detects instances where a non-root user executes the 'sudo' command in combination with 'tee'. This pattern is frequently used to bypass file permission restrictions by appending or writing content to sensitive system files that would normally require root privileges, a common method for privilege escalation or maintaining persistence.
This rule detects the modification or creation of sensitive Linux files often used for persistence, such as cron job configurations, system boot scripts, and custom system tools. It monitors for file writing and process execution events involving specific paths and filenames identified as associated with persistence mechanisms or monitoring tools.
This rule detects two potentially malicious activities related to SSH: first, specific utilities (e.g., luserput, blacklist, ssh) initiating outbound connections to internal network CIDR ranges over port 22, which may indicate lateral movement; second, the creation or modification of the .ssh/authorized_keys file, which is a common technique for establishing persistence or unauthorized access.
Detects potential privilege escalation and unauthorized file modification by the 'egnyteservice' account. The rule monitors Syslog for sudo usage involving the 'tee' utility with 'NOPASSWD' configuration, or suspicious writes to critical system directories such as /usr/sbin/ and /etc/cron.d/, which are indicative of VerdantBamboo threat actor activity on edge appliances.
Detects Python processes spawned by development tools (VSCode, Cursor) or Node.js that are engaged in suspicious activities, specifically communicating with the Telegram Bot API or uploading large amounts of data, often accompanied by file staging (ZIP archive creation). This behavior is characteristic of the InvisibleFerret information stealer.
Clipboard hijacking malware for cryptocurrency address substitution
This rule detects various techniques used by an adversary to clear, truncate, or delete system and audit logs to hide their activity. It monitors for commands targeting Windows Event Logs (wevtutil, Clear-EventLog), Linux system logs (/var/log/ via rm/shred), and journalctl vacuuming, as well as the deletion of .evtx files. It further enriches these detections by identifying if the process was spawned by suspicious parent processes often associated with remote access, lateral movement, or command-and-control activity.
Detects anomalous SSH daemon (sshd) activity characterized by the installation or execution of a non-standard sshd binary (often located in /tmp or /var/tmp) or the execution of sshd with suspicious command-line arguments (custom ports or custom configuration files), followed by modifications to the authorized_keys file. This behavior is indicative of unauthorized persistence or backdoor access.
This rule monitors for Node.js processes accessing sensitive configuration files, environment files, or credential stores (such as AWS credentials). It also triggers when command line arguments or file contents contain patterns indicative of infostealer activity, including specific indicators related to the Polymarket campaign or credentials collection functions like Git or Docker credential harvesting.
This rule detects network connections from devices to a predefined list of known malicious domains. It identifies attempts by internal systems to communicate with command and control servers, phishing sites, or other infrastructure associated with threat actors.
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