Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Node.js or Electron processes executing from suspicious paths (e.g., Temp, Roaming) while interacting with web browser credential files or executing commands consistent with automated credential dumping tools.
Detects a suspicious sequence of activities where PowerShell downloads or extracts specific zip files (node.zip, build.zip) to staging directories, followed by the execution of associated binaries like node.exe, winpty-agent.exe, or winpty.dll from those same locations. This behavior is indicative of an adversary staging and executing tooling within temporary user directories.
Detects a hardcoded PEM RSA public key embedded alongside JSCeal-specific compiled V8 bytecode obfuscation artifacts, used to encrypt exfiltrated data or C2 communications
Detects Node.js or related helper processes executing shell commands (cmd.exe, powershell.exe) or other processes where the execution involves the 'withCreateProcessUser' argument. This often indicates attempts to bypass execution restrictions or run malicious payloads by utilizing Node.js as an execution proxy within unconventional or temporary directories.
This rule detects suspicious activity associated with JSCeal proxy handlers. It identifies Node.js or Electron processes running from non-standard, user-writable directories (such as AppData, ProgramData, or Temp folders) that actively delete or modify browser cookie files within standard web browser profile paths, often indicative of session hijacking or data tampering.
This rule detects suspicious activity associated with JSCeal proxy handlers. It identifies Node.js or Electron processes running from non-standard, user-writable directories (such as AppData, ProgramData, or Temp folders) that actively delete or modify browser cookie files within standard web browser profile paths, often indicative of session hijacking or data tampering.
Detects periodic screen capture activity performed by Node.js or Electron-based processes, correlated with subsequent outbound network connections to public IP addresses within a short timeframe. This behavior is indicative of the JSCeal malware's surveillance and exfiltration module, where local reconnaissance via screenshotting is followed by data transmission.
Detects when a Node.js process initiates a low-level keyboard hook (WH_KEYBOARD_LL or WH_KEYBOARD) while running from common user-writable directories, which may indicate malicious keylogging activity by a suspicious process.
This rule monitors for the execution of Node.js processes involving a file named 'parser.js' and a component or argument referred to as 'ldata'. This behavior is characteristic of specific application workflows, but when observed as a standalone or unusual process, it may indicate unauthorized script execution or the activity of a malicious node-based tool.
Detects anomalous child process creation (e.g., cmd.exe, powershell.exe, rundll32.exe) spawned by the VMware host process 'vmware-vmx.exe'. This behavioral pattern is a high-confidence indicator of potential guest-to-host virtual machine escape, specifically monitoring for activity associated with vulnerabilities like CVE-2026-59346.
Detects an internal/compromised mailbox sending phishing-style email to multiple employees, the technique CISA's red team used to gain initial workstation access via a trusted internal sender.
This rule detects potential data exfiltration by monitoring write activity to external USB storage devices. It identifies scenarios where a high number of files (more than 25) or a significant total volume of data (more than 100MB) is written to a connected USB device within a short timeframe.
Detects the issuance of certificates using templates associated with the Certificate Request Agent. This is a critical component of the ESC3 attack vector, where an adversary first acquires an Enrollment Agent certificate and subsequently uses it to enroll for certificates on behalf of other privileged users or principals.
Detects the use of the 'netsh wlan show profile' command with the 'key=clear' argument, which is used to display the plaintext passwords of stored WiFi profiles on a Windows system.
This rule detects unauthorized modifications to browser search provider settings in the Windows Registry, targeting keys associated with Chrome and Microsoft Edge search configuration. This activity is often indicative of browser hijacking or search engine redirection campaigns, such as the NinjaMare malware.
This rule detects a suspicious sequence of events where a process with a name resembling an updater (AutoUpdate.exe or au.exe) accesses a specific remote configuration file from a herokuapp.com URL, followed by the creation or modification of specific application binaries (e.g., InstaTime.exe, ffmpegsumo.dll). This pattern is indicative of a supply chain compromise or an automated software update hijacking where malicious binaries are staged to replace legitimate application components.
This is something I have seen many times during my years working in cybersecurity: users download software with a filename they recognize and trust, but the file behind that name can be something completely different.
Monitoring the filename is not enough. Monitoring the hash is better, but legitimate software changes constantly: new version, new SHA256, another hash to validate and maintain. So I started thinking about how AI agents could help us here — not by deciding what is malicious, but by doing the repetitive work for us.
For this research, I built an AI-maintained baseline of legitimate software and used it from KQL to hunt two different scenarios:
🎯 Known filename + Unknown hash + Untrusted download source
Monitoring the filename is not enough. Monitoring the hash is better, but legitimate software changes constantly: new version, new SHA256, another hash to validate and maintain. So I started thinking about how AI agents could help us here — not by deciding what is malicious, but by doing the repetitive work for us.
For this research, I built an AI-maintained baseline of legitimate software and used it from KQL to hunt two different scenarios:
🎯 Known filename + Unknown hash + Untrusted download source
Detects the execution or initiation of a process named 'PrettyPrague.exe' while running with SYSTEM level privileges (AccountSid: S-1-5-18) from directories associated with Avast antivirus software. This activity is indicative of potential privilege escalation or malicious activity masquerading within legitimate security software paths.
This rule detects potential bulk data exfiltration by monitoring for high volumes of file access events (FileAccessed, FileRead, FileModified) originating from the 'doc_helper.aspx' file-management web shell. It summarizes activity by device and user account, flagging instances where over 100 unique files are touched within a short timeframe, which is indicative of automated collection and exfiltration activities.
Detects unauthorized attempts to dump the process memory of the Local Security Authority Subsystem Service (LSASS), a common technique used by attackers to harvest credentials from memory. This includes the use of legitimate diagnostic tools like procdump and comsvcs.dll, as well as the identification of resulting dump files in directory paths associated with LSASS.
Detects network activity associated with the CurlRAT malware during its beaconing phase. The rule monitors for process command line arguments containing 'writeservice_info' or 'atd_get_system_info', which indicate the collection of system information, and correlates this with network connections that potentially transmit a ~10KB data payload characteristic of its check-in mechanism.
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