Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Node.js processes making outbound network connections to the AWS Instance Metadata Service (IMDS) IP address 169.254.169.254, which may indicate unauthorized data collection or discovery via server-side request forgery (SSRF).
Detects SVG image files that contain embedded HTML or script payloads, often used as an initial delivery vector to smuggle malicious code (such as downloaders or web redirects) into a target environment. This technique has been associated with the delivery of various remote access trojans (RATs).
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.
This rule detects the presence of compiled Python 3.10 bytecode (.pyc) files that have been renamed to or disguised with a .cat extension. It specifically looks for the 'settingenv.cat' file associated with the Chinotto backdoor, as well as associated C2 URL patterns or command-line artifacts indicative of its download and execution chain.
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 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 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 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.
This rule detects potential automated network scanning activity originating from hosts that have recently executed cPanel-related processes or binaries. It flags devices that exhibit a high frequency of connection attempts across a diverse range of remote ports and IP addresses within a short timeframe after cPanel interaction, which is characteristic of worm propagation or automated exploitation attempts targeting web server management interfaces.
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
This rule detects command-line activity where a remote resource is downloaded via 'curl', decoded using 'base64', and then piped directly into a shell interpreter (sh, bash, or zsh). This pattern is commonly used in fileless execution or stage-one payload delivery by attackers to execute malicious scripts directly from memory.
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.
Detects malicious JavaScript/npm payloads that attempt to evade static analysis and string-based detection signatures by dynamically reconstructing sensitive strings at runtime using V8 string-object construction or obfuscated string tables.
This rule monitors for the presence of a specific, known malicious package (@dforge-core/dforge-mcp version 0.2.21) being referenced in file paths, indicative of a software supply chain compromise or the distribution of malicious dependency artifacts.
Detects instances where a process attempts to delete its own executable file on disk. This behavior is often associated with malware or adversary tools attempting to remove their footprint post-execution or as part of a cleanup routine.
Detects Node.js processes executing suspicious command lines that involve fetching and executing remote JavaScript code. This behavior is indicative of a loader or downloader script commonly used to pull and evaluate malicious payloads at runtime, specifically referencing indicators associated with 'GHAPPIER'.
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