Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule identifies installations of TeamViewer software that are vulnerable to specific CVEs (CVE-2026-19743, CVE-2026-92368, CVE-2026-92369, CVE-2026-92370, CVE-2026-92371) by analyzing the 'DeviceTvmSoftwareInventory' dataset. It checks for versioning patterns against known vulnerable build numbers across Windows, Linux, and macOS platforms to pinpoint systems requiring patching.
This rule identifies installations of TeamViewer software that are vulnerable to specific CVEs (CVE-2026-19743, CVE-2026-92368, CVE-2026-92369, CVE-2026-92370, CVE-2026-92371) by analyzing the 'DeviceTvmSoftwareInventory' dataset. It checks for versioning patterns against known vulnerable build numbers across Windows, Linux, and macOS platforms to pinpoint systems requiring patching.
This rule identifies installations of TeamViewer software that are vulnerable to specific CVEs (CVE-2026-19743, CVE-2026-92368, CVE-2026-92369, CVE-2026-92370, CVE-2026-92371) by analyzing the 'DeviceTvmSoftwareInventory' dataset. It checks for versioning patterns against known vulnerable build numbers across Windows, Linux, and macOS platforms to pinpoint systems requiring patching.
This rule detects anomalous spikes in network connections from a device to major LLM provider API endpoints (OpenAI, Azure OpenAI, Anthropic) by establishing a baseline for each device and identifying deviations exceeding a statistical threshold. Such behavior may indicate unauthorized data exfiltration or mass data processing using LLM services.
This rule monitors the software inventory for vulnerable versions of OpenSSL and WolfSSL libraries. Specifically, it flags instances of WolfSSL prior to version 5.9.4, which are susceptible to peer-authentication bypass vulnerabilities. The rule is intended for patch management and asset exposure tracking rather than detecting active exploitation.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
This detection identifies execution of the file synchronization utility "rclone".
It leverages Cisco Network Visibility Module logs, specifically flow data in order to capture process executions
initiating network connections.
While rclone is a legitimate command-line tool for syncing data to cloud storage providers, it has been widely abused by threat actors for data exfiltration.
This analytic inspects process name and arguments for rclone and flags usage of suspicious flags.
If matched, this could indicate malicious usage for stealthy data exfiltration or cloud abuse.
It leverages Cisco Network Visibility Module logs, specifically flow data in order to capture process executions
initiating network connections.
While rclone is a legitimate command-line tool for syncing data to cloud storage providers, it has been widely abused by threat actors for data exfiltration.
This analytic inspects process name and arguments for rclone and flags usage of suspicious flags.
If matched, this could indicate malicious usage for stealthy data exfiltration or cloud abuse.
This analytic identifies the use of Chromium-based browsers (like Microsoft Edge) running in headless mode with the `--dump-dom` argument.
This behavior has been observed in attack campaigns such as DUCKTAIL, where browsers are automated to stealthily download content from the internet using direct URLs or suspicious hosting platforms.
The detection focuses on identifying connections to known file-sharing domains or direct IPs extracted from command-line arguments and cross-checks those against the destination of the flow.
Since it leverages Cisco Network Visibility Module telemetry, the rule triggers only if a network connection is made.
This behavior has been observed in attack campaigns such as DUCKTAIL, where browsers are automated to stealthily download content from the internet using direct URLs or suspicious hosting platforms.
The detection focuses on identifying connections to known file-sharing domains or direct IPs extracted from command-line arguments and cross-checks those against the destination of the flow.
Since it leverages Cisco Network Visibility Module telemetry, the rule triggers only if a network connection is made.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
Detects instances where a browser extension is installed, followed within a two-hour window by the browser process making network connections to generative AI service domains. This behavior is indicative of potential malicious browser extension activity, such as scraping or exfiltrating session/browsing data to third-party AI backend services.
This rule monitors package installation logs for indicators of packages potentially generated or suggested by AI tools (e.g., Copilot, code assistants) being installed in a target environment. It specifically looks for a low volume of installations (<=3) for packages that have been published within the last 14 days, which is a pattern often associated with the 'Publish Hallucinated Entities' technique in AI systems, where malicious or hallucinated code packages are introduced into the supply chain.
Detects non-CLI and non-DevOps utility processes accessing sensitive cloud credential and configuration files, including AWS SSO caches, AWS credentials, kubeconfig, and gcloud configuration files. This behavior is indicative of credential theft or discovery by infostealers attempting to gain unauthorized access to cloud environments.
Detects high-frequency outbound network connections to known large language model (LLM) API providers (OpenAI, xAI, Anthropic) originating from a single endpoint within a short timeframe. This activity is indicative of potential API key theft, where an attacker uses a compromised legitimate key to exhaust credit quotas or exfiltrate data via unauthorized API calls, bypassing the intended application.
Detects execution of encoded commands in macOS terminal environments that originate from clipboard content mimicking a reCAPTCHA verification process. This pattern often involves users being social-engineered into pasting a malicious string that decodes a payload and pipes it directly into a shell interpreter (sh/bash/zsh). The rule monitors for the co-occurrence of base64/openssl decoding commands and shell execution pipes, while filtering out known legitimate software installation patterns.
Detects potential 'ClickFix' activity where a browser or file explorer process initiates a shell (e.g., cmd, powershell, osascript) with command arguments indicative of remote script execution, obfuscated commands, or web-based payload downloading. This pattern matches known social engineering tactics that entice users to copy-paste malicious code into a system shell.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Identifies suspicious processes performing beacon-like network communications with rare external destinations. The rule detects consistent hourly activity intervals and high connection counts to endpoints rarely contacted by other devices in the organization, serving as a heuristic for automated C2 beaconing.
Detects unauthorized processes attempting to read sensitive wallet extension storage files or desktop application wallet data folders, a behavior characteristic of the Psychedelic Stealer malware designed to harvest cryptocurrency credentials prior to exfiltration.
This rule detects potential persistence via Python environment variable manipulation (PYTHONPATH) followed by suspicious network activity from a child process of python.exe. It specifically looks for the modification of the PYTHONPATH environment variable using PowerShell on Windows or shell configuration files on Unix-like systems, followed by a python.exe-initiated network connection (like curl) to external domains, which may indicate the execution of a malicious sitecustomize.py hook.
This rule detects instances where Python package management processes (pip or python) perform build or installation tasks, immediately followed by an outbound network connection initiated by the same process tree to a non-PyPI domain. This behavior is indicative of a supply chain attack where a malicious package contains code (e.g., in setup.py) that beacons to an attacker-controlled server upon installation.
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