Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
694 detections
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Detects the indexed-btree npm malware loader embedded in BTree.prototype.set that spawns a detached hidden Node.js child process and references sharedLoad.min.js
This rule detects instances where a Node.js process spawns a child Node.js process from a 'node_modules' directory using configuration flags typically associated with hidden, detached background tasks (e.g., 'detached', 'stdio', 'windowsHide', 'ignore'). It explicitly excludes common Node.js process managers and build tools to reduce false positives associated with legitimate development activities.
Detects the creation of a scheduled task using schtasks.exe where the command line involves 'MicrosoftNodeRuntimeUpdater' and references 'node.exe' or '.js' files, specifically when initiated by script engines like node.exe, wscript.exe, or cscript.exe, while excluding known legitimate nodejs installation paths.
Detects execution and network activity related to the PamStealer / Wavel malware campaign, including the JXA dropper and associated C2 infrastructure. The rule monitors for known malicious file hashes (e.g., JXA dropper, pkgunpack utility) and network connections to identified lure, distribution, and C2 domains and URLs used by the campaign.
Detects execution and network activity related to the PamStealer / Wavel malware campaign, including the JXA dropper and associated C2 infrastructure. The rule monitors for known malicious file hashes (e.g., JXA dropper, pkgunpack utility) and network connections to identified lure, distribution, and C2 domains and URLs used by the campaign.
This rule detects the use of 'npm' or 'node' to install packages from a registry other than trusted, standard registries (e.g., npmjs.org, GitHub). It specifically monitors command lines involving 'claude-code' that utilize custom, non-standard registry URLs. This behavior is indicative of potential supply chain attacks or the installation of unauthorized/malicious dependencies.
Detects BeaverTail JavaScript-based loader malware hidden inside NPM packages, distributed via GitHub or Bitbucket during fake technical interviews
This rule detects the LausivLoader initial-stage obfuscated JavaScript dropper. It identifies the malware either by specific file hashes (MD5/SHA256) or by detecting characteristic behaviors: small file size (<1MB), the presence of 'String.fromCharCode' obfuscation techniques, excessive junk comments, and supply-chain related lure strings.
Detects attempts to bypass or tamper with the Antimalware Scan Interface (AMSI) via direct memory modification or through script command-line arguments. The rule monitors DeviceEvents for AMSI-specific events (e.g., buffer patching, provider tampering) and looks for common PowerShell and scripting engine command-line patterns indicative of AMSI bypass techniques.
Detects instances where wscript.exe spawns conhost.exe with a headless flag, which in turn executes PowerShell with hidden, encoded command-line arguments. This pattern is often indicative of multi-stage malware loaders attempting to obfuscate execution and bypass security monitoring.
Detects potential zero-click administrator session hijacking by correlating a logged-in administrator accessing a page containing user-generated content (e.g., comments) followed immediately by a sensitive administrative action (e.g., plugin installation, user creation) within a 5-second window, suggesting automated script execution via an autofocus/onfocus handler triggered by the admin's browser.
Detects potential zero-click administrator session hijacking by correlating a logged-in administrator accessing a page containing user-generated content (e.g., comments) followed immediately by a sensitive administrative action (e.g., plugin installation, user creation) within a 5-second window, suggesting automated script execution via an autofocus/onfocus handler triggered by the admin's browser.
Detects potential zero-click administrator session hijacking by correlating a logged-in administrator accessing a page containing user-generated content (e.g., comments) followed immediately by a sensitive administrative action (e.g., plugin installation, user creation) within a 5-second window, suggesting automated script execution via an autofocus/onfocus handler triggered by the admin's browser.
Detects high-frequency, automated interactions with popular AI assistant web interfaces (e.g., Gemini, Perplexity, Claude, Copilot) from common browser processes. This pattern often indicates unauthorized automated data submission or scraping, which may follow or facilitate automated exfiltration of browser-resident data.
Detects high-frequency, automated interactions with popular AI assistant web interfaces (e.g., Gemini, Perplexity, Claude, Copilot) from common browser processes. This pattern often indicates unauthorized automated data submission or scraping, which may follow or facilitate automated exfiltration of browser-resident data.
This rule detects the execution of npm or node.js commands involving specific, potentially malicious package names or internal project naming patterns often associated with dependency confusion or supply chain attacks. It monitors npm/node CLI arguments for targeted library names or installation commands that deviate from standard organizational behavior.
This rule detects the execution of npm or node.js commands involving specific, potentially malicious package names or internal project naming patterns often associated with dependency confusion or supply chain attacks. It monitors npm/node CLI arguments for targeted library names or installation commands that deviate from standard organizational behavior.
This rule detects the execution of npm or node.js commands involving specific, potentially malicious package names or internal project naming patterns often associated with dependency confusion or supply chain attacks. It monitors npm/node CLI arguments for targeted library names or installation commands that deviate from standard organizational behavior.
This rule detects the execution of npm or node.js commands involving specific, potentially malicious package names or internal project naming patterns often associated with dependency confusion or supply chain attacks. It monitors npm/node CLI arguments for targeted library names or installation commands that deviate from standard organizational behavior.
Detects the execution of wscript.exe or cscript.exe with VBScript or JScript files from user-writable and temporary directories, such as %TEMP%, %APPDATA%, and Downloads folders. This pattern is commonly associated with the execution of malicious droppers and loaders in malspam campaigns.
Detects high-frequency, automated interactions with popular AI assistant web interfaces (e.g., Gemini, Perplexity, Claude, Copilot) from common browser processes. This pattern often indicates unauthorized automated data submission or scraping, which may follow or facilitate automated exfiltration of browser-resident data.
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