Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects the execution of common Node.js package managers (npm, yarn, pnpm) attempting to install specific dependencies that match known patterns of malicious or typosquatted packages often used in supply chain attacks. The detection focuses on suspicious package names that mimic common data structure library naming conventions.
Detects the presence of an XMRig cryptocurrency miner masquerading as the systemd-logind process. The rule identifies processes named 'systemd-logind' that either reside in an incorrect file path ('/usr/sbin/systemd-logind') or utilize command-line arguments characteristic of XMRig mining activity. This behavior is associated with the CARBONATO botnet.
Detects the execution of TruffleHog, an automated secret-scanning CLI tool, often utilized by threat actors to harvest sensitive credentials from local filesystems, repositories, environment variables, or CI/CD configuration files following unauthorized access.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
Detects the execution of the 'fanout.sh' utility from the /tmp directory, which subsequently spawns 'sshpass' child processes to perform rapid, non-interactive SSH authentication attempts ('StrictHostKeyChecking=no') against multiple internal hosts within a 10-minute window. This behavior is indicative of automated lateral movement, specifically observed by threat actor UNC6240.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects the installation of machine learning or AI software dependencies (pip, conda, npm, poetry) that either pull from unauthorized registries or match known malicious/typosquatting naming patterns (e.g., LiteLLM supply-chain compromise). The rule also monitors for newly published packages that have been flagged as suspicious.
Detects attempts by AI agent code-interpreter or sandbox processes to break out of their containerized environment. The rule monitors for the execution of common container escape techniques, such as namespace manipulation (nsenter, unshare), mounting host filesystems, accessing sensitive host devices like /dev/kmsg or /dev/mem, or referencing host-level paths from within an isolated AI agent execution process.
Detects the execution of suspicious child processes (e.g., cmd.exe, powershell.exe, bash, wget) spawned by processes associated with public-facing appliances like Fortinet SSL-VPN, Veeam Backup & Replication, Citrix ADC, and cPanel/WHM. This behavior is often indicative of exploitation of public-facing applications (T1190) for initial access, frequently associated with ransomware actors targeting unpatched infrastructure.
Detects the execution of suspicious child processes (e.g., cmd.exe, powershell.exe, bash, wget) spawned by processes associated with public-facing appliances like Fortinet SSL-VPN, Veeam Backup & Replication, Citrix ADC, and cPanel/WHM. This behavior is often indicative of exploitation of public-facing applications (T1190) for initial access, frequently associated with ransomware actors targeting unpatched infrastructure.
Detects instances where a Node.js process spawns another Node.js process to execute a JavaScript file located within the node_modules directory. This pattern is commonly observed when malware or malicious scripts attempt to execute payloads from within project dependencies, potentially hiding malicious activity within seemingly legitimate library paths.
Detects the creation of files or directories under specific paths (/.openclaw/ and /.sckit/) used by the sckit Go-based worm. This worm, distributed via compromised npm and PyPI packages, uses these directories to track state and coordinate execution on infected Linux systems.
Detects the execution of bash commands containing base64 decoding instructions, often used by adversaries to decode and execute obfuscated scripts or payloads. The rule specifically looks for command lines invoking base64 along with keywords like 'echo' or 'reCAPTCHA', which are commonly associated with malicious droppers or phishing-related scripts.
Detects instances where package management tools (pip, npm, docker) spawn child processes that immediately attempt to read sensitive files (SSH keys, cloud/container credentials) or perform outbound network connections. This behavior is indicative of a trojanized supply-chain package executing malicious post-install logic to exfiltrate secrets or establish initial communication with a C2 server.
Detects anomalous child processes spawned by a Langflow server process that subsequently search for sensitive information, such as cloud credentials or database connection strings, within the command line arguments. This behavioral pattern is indicative of remote code execution exploitation followed by credential harvesting, potentially targeting vulnerabilities like CVE-2026-33017.
Detects Node.js processes, such as npm package postinstall hooks, spawning shell commands used for host enumeration. This includes accessing system environment variables, network configurations, and filesystem metadata, a pattern frequently utilized by malicious packages for initial reconnaissance post-installation.
Detects CARBONATO botnet activity where malicious processes are masquerading as legitimate Linux system components. This includes XMRig mining software running as /usr/sbin/systemd-logind, process arguments attempting to mimic kernel kworker threads, or processes running from directories associated with netd-svc/resolved container images.
Detects evidence of persistence mechanisms targeting Docker environments on Linux systems. The rule identifies suspicious modifications using crontab, systemctl (service creation/start), and chattr (to make files immutable), specifically monitoring for artifacts related to '.docker-network-monitor' and 'docker-network-resolver'. It flags systems where multiple such configuration changes occur within a single day.
Detects unauthorized file creation within the /root/.hermes/loot/ directory and specific path combinations associated with the CARBONATO botnet's Hermes Agent. This activity is indicative of credential harvesting, specifically the exfiltration of AI API keys for various LLM providers.
Detects the execution of the XMRig cryptominer masquerading as the legitimate systemd-logind service. The rule identifies suspicious command-line arguments indicative of mining activity, or instances where systemd-logind is spawned from unauthorized parent processes such as shell interpreters, container engines, or nsenter, which is often characteristic of post-exploitation activity like privileged Docker container escapes.
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