Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 modifications to the .zshrc configuration file followed by shell commands interacting with the file, or the execution of commands that source the .repair-run script within a shell environment. This pattern is indicative of potential persistence mechanisms where an adversary modifies startup scripts to maintain access or execute malicious code upon shell initialization.
This rule detects modifications or creation of Git hooks ('pre-commit', 'post-checkout') within local repository directories. Attackers can leverage these hooks as an execution trigger for arbitrary code whenever standard Git actions occur, facilitating persistence or privilege escalation on compromised developer machines.
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 execution of Node.js processes loading specifically named JavaScript files ('sharedLoad.min.js') within the 'node_modules/indexed-btree' directory, or file modifications involving these specific paths, which may indicate a malicious loader or supply chain compromise attempt.
This rule detects instances where a Node.js process spawns a child process, also running as node.exe, that attempts to execute a specific JavaScript file path (sharedLoad.min.js or its extended version). This behavior is characteristic of certain Node.js-based applications or potentially malicious scripts using shared loaders for modular execution.
Detects the SCKit Go-based credential-stealing implant found in compromised npm and PyPI packages. The detection logic focuses on unique build-info module paths, specific C2 encryption protocol headers, and embedded campaign identifiers related to 'memos-cloud-openclaw-plugin' and 'MemoryOS' supply chain compromises.
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.
This rule monitors application, build system, and dependency scanning logs for references to known-malicious versions of the @memtensor/memos-cloud-openclaw-plugin (npm) and MemoryOS (PyPI) packages. These versions contain the sckit Go worm and are associated with automated supply chain attacks involving the reuse of stolen credentials, indicated by the 'exact-ref-one-use-NPM_TOKEN' marker.
Detects npm package publish events that lack 'gitHead' provenance metadata. The absence of this field indicates that the package was likely published from a developer's local machine rather than through an authorized CI/CD pipeline, a technique used in supply chain attacks to bypass CI-gated controls.
Detects a suspected malicious activity pattern known as the Veltrix Capital fake job-lure. The rule monitors for a sequence where a user clones a job-task repository (containing 'veltrix-capital' in the command) followed by the installation of a suspicious dependency (using names like 'graph*' or 'big*' with npm or pip) within a 30-minute window on the same device.
Detects a suspicious sequence where a Go toolchain is used to fetch dependencies, followed by a 'go run' execution that spawns a child process which initiates an outbound network connection shortly thereafter. This behavior is indicative of a 'hidden entry point' pattern where legitimate development tools are abused to download, compile, and execute a second-stage remote access trojan (RAT) or malicious payload.
Detects network connections to the Slack API (api.slack.com) initiated by processes executing from temporary directories commonly used by the Go build system ('go-build'). This pattern is indicative of a second-stage RAT or malicious payload compiled on-the-fly using 'go run' or similar mechanisms, utilizing Slack's infrastructure for C2 communication.
Detects instances where common scripting or development language runtimes (Go, Node.js, WScript, CScript) access sensitive configuration or credential files, such as AWS credentials, Kubernetes configs, SSH keys, or environment files. This activity is often indicative of credential harvesting or unauthorized access to sensitive secrets by potentially malicious scripts or processes.
Detects a successful login attempt by the user account 'p2pwn' targeting port 37777. Port 37777 is commonly associated with DVR/NVR surveillance equipment and is frequently targeted by brute-force attacks and botnets attempting to gain unauthorized access to embedded devices.
Detects JavaScript loaders associated with the Exvicy/ErrTraffic campaign. The rule identifies obfuscated code that utilizes atob() for Base64 decoding, single-byte XOR operations, TextDecoder for content processing, and dynamic code execution via 'new Function()'. This pattern is commonly used to inject malicious payloads into compromised WordPress sites.
Detects JavaScript code within HTML pages designed to block browser developer tools (F12, Inspect, View Source) using keydown event listeners. This technique is commonly associated with ClickFix-style phishing campaigns to prevent users or security analysts from inspecting malicious page content or fake CAPTCHA overlays.
Detects instances where a non-browser process initiates connections to commercial LLM provider APIs (DeepSeek, OpenRouter, Mistral) followed by a connection to Discord CDN/Webhook endpoints within a 15-minute window. This behavior is indicative of a process acting as an autonomous C2 agent that exfiltrates data after receiving instructions or processing information via an LLM.
Detects malicious shell commands attempting to perform supply-chain persistence via environment variable manipulation in CI/CD environments. The rule identifies processes injecting BASH_ENV into GITHUB_ENV, a known technique used by the SCKIT supply-chain worm to perform credential harvesting during CI pipeline execution.
Detects malicious shell commands attempting to perform supply-chain persistence via environment variable manipulation in CI/CD environments. The rule identifies processes injecting BASH_ENV into GITHUB_ENV, a known technique used by the SCKIT supply-chain worm to perform credential harvesting during CI pipeline execution.
Detects the execution of the 'sckit' Go implant, a malicious binary associated with trojanized '@memtensor/memos-cloud-openclaw-plugin' or 'MemoryOS' packages. The rule identifies instances where this binary is spawned as a child process of a language runtime (Node.js or Python) when the execution originates from specific directory paths associated with these packages, or when the parent process command line indicates these packages are being initialized.
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