Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects incoming HTTP requests where the 'pagename' parameter contains directory traversal sequences (e.g., '../', '..%2f'). These attempts are typically indicative of automated vulnerability scanning or exploitation attempts targeting WordPress page-template resolution logic, specifically related to CVE-2026-87902.
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 anomalous child process execution (e.g., shells, curl, python) or the creation of web-accessible script files (.jsp, .php, .aspx) originating from common VPN gateway or edge appliance service processes. This behavior is indicative of potential exploitation of a remote-facing appliance or webshell deployment.
Detects anomalous activity in a CI/CD environment where a runner process accesses sensitive credential files (such as PyPI tokens or GitHub secrets) followed closely by a network connection to public package registry domains, potentially indicating a supply-chain compromise where stolen credentials are used to publish malicious packages.
Detects Python processes (python.exe, python3.exe, pythonw.exe) loading native library files (.pyd or .so) followed by file access to sensitive browser credential and cookie storage locations (e.g., Login Data, Cookies, Local State). This pattern is characteristic of infostealer activity, where a Python script uses a native module to decrypt or extract browser-stored credentials.
Detects instances where the Cursor IDE (or associated helper processes) spawns a child process or executes a command involving a specific 'DevOps-Automation/cloudshield' path. This monitors for potential abuse of IDE environments to trigger sensitive automation scripts, possibly indicating malicious use of IDE tooling or unauthorized development activities.
Detects anomalous Telnet (TCP port 23) traffic associated with Mirai-variant botnet propagation, such as scanning for new targets or inbound compromise attempts, often following cPanel/WHM vulnerabilities.
Detects non-browser automated HTTP clients (e.g., Python, Wget, Curl, Scrapy) downloading audio/video files from corporate public-facing pages such as Investor Relations, Press Releases, and Executive profiles. This activity is indicative of malicious reconnaissance used to harvest voice or video samples for synthetic media (deepfake) creation for future social engineering or impersonation attacks.
Detects process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
Detects instances where a single process on a device communicates with two or more distinct hosted LLM provider APIs (OpenAI, Anthropic, DeepSeek, Google) within a 24-hour window. This behavior is indicative of multi-model orchestration or 'consensus' techniques, which may be employed by sophisticated implants for automated tasking, data processing, or evasion.
Detects instances where a single process on a device communicates with two or more distinct hosted LLM provider APIs (OpenAI, Anthropic, DeepSeek, Google) within a 24-hour window. This behavior is indicative of multi-model orchestration or 'consensus' techniques, which may be employed by sophisticated implants for automated tasking, data processing, or evasion.
Detects anomalous Telnet (TCP port 23) traffic associated with Mirai-variant botnet propagation, such as scanning for new targets or inbound compromise attempts, often following cPanel/WHM vulnerabilities.
Detects sensitive cPanel/WHM administrative commands or system file modifications occurring within one hour of a suspected authentication bypass event, indicative of potential post-exploitation activity related to CVE-2026-41940.
Detects network connections or DNS queries to known suspicious domains associated with command and control infrastructure.
Detects network connections or DNS queries to known suspicious domains associated with command and control infrastructure.
Detects network connections or DNS queries to known suspicious domains associated with command and control infrastructure.
Detects execution of MeshAgent binaries when initiated by common scripting or application processes (e.g., java, sh, bash, python) or when displaying suspicious command-line patterns indicative of unauthorized remote access or download activity, particularly in environments like WebLogic or PeopleSoft.
Detects malformed HEIF/HEIC image files with anomalous ftyp/box structure consistent with the libheif memory corruption exploit (CVE-2026-32882) used against Discourse forum image upload endpoints
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
This rule detects incoming HTTP POST requests to the '/api/v1/validate/code' endpoint of a Langflow instance. The rule specifically looks for a JSON key 'code' containing a large, potentially base64-encoded string, which indicates an attempt to exploit CVE-2025-3248 to achieve unauthenticated remote code execution.
This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
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