Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects non-browser processes (e.g., non-standard executables) attempting to read sensitive browser data files, specifically Login Data, Cookies, or Firefox profile data. This behavior is highly characteristic of credential-stealing malware or unauthorized data exfiltration tools accessing browser-stored secrets.
Detects unauthorized non-browser processes attempting to access sensitive credential and session cookie files associated with major web browsers such as Chrome, Edge, and Firefox. This behavior is indicative of credential harvesting techniques often utilized by infostealer malware, including variants of the Millenium RAT, to extract stored passwords and session tokens.
Detects attempts by unsigned processes to read or modify sensitive web browser credential files (Login Data or logins.json) typically used by Google Chrome, Microsoft Edge, and Mozilla Firefox. This behavior is indicative of credential dumping or information stealing activities by unauthorized or malicious software.
Detects the CastleLoader Python process rapidly scraping Chromium SQLite databases (Login Data, Cookies) to steal stored credentials and session cookies. The rule identifies Python processes accessing multiple browser credential/session databases within a one-minute window.
Detects potential financial data exfiltration by monitoring for bulk downloads of files with financial keywords from SaaS/Office365 platforms, and identifies unauthorized processes attempting to access sensitive financial application data files (.qbw, .qbb, etc.) on local systems.
Detects the use of the 'cp' command within a Docker container context to access files outside the current working directory, identified by the usage of parent directory traversal (..). This pattern is often associated with container escape attempts or unauthorized file access within a containerized environment.
Detects unauthorized processes (Python, Node.js) accessing sensitive files such as browser login data, cookie stores, wallet files, and SSH/AWS keys, which are common indicators of credential theft and data exfiltration.
Detects anomalous activity associated with information stealing malware, including unauthorized access to browser credential files (Login Data, Cookies), remote thread injection into browser processes, outbound connections to the Telegram API by non-browser processes, and rapid bulk access to multiple sensitive credential or crypto wallet files.
Detects instances where Python or Node.js processes access common cloud provider credential files (e.g., AWS, GCP, Azure). This behavior is often associated with infostealer campaigns, such as the 'Contagious Interview' campaign, where adversaries attempt to harvest stored cloud credentials for further compromise.
Detects instances where scripting or development processes (e.g., node.exe, python.exe, code.exe) access sensitive SSH private key files or PEM files, followed by an immediate outbound network connection to a non-private IP address within 60 seconds. This activity is indicative of credential exfiltration or unauthorized use of stolen keys.
This rule detects scenarios where development tools such as VS Code (Code.exe) or npm spawn multiple child processes of Python or Node.js interpreters within a short time window, followed by those same interpreters accessing sensitive credential files (e.g., SSH keys, browser login data, or cloud configuration files). This pattern is indicative of a developer workstation compromise or a supply chain attack involving malicious code execution.
Detects potential exploitation of CVE-2026-20127 on SD-WAN controllers, specifically unauthorized SSH authentication to the vmanage-admin account followed by suspicious NETCONF activity, usage of unauthorized tenant list CSV files, or execution of suspicious tenant-upload scripts.
Detects a network-connected Linux process exhibiting beaconing behavior (high-frequency external connections) that is subsequently correlated with unauthorized file access to sensitive Linux configuration files (/etc/passwd, /etc/shadow, or authorized_keys). This rule filters out common system processes and standard management binaries, targeting suspicious command-and-control activity combined with credential theft.
Detects unauthorized processes reading AI assistant configuration files (e.g., Claude, Cursor, Gemini, VS Code) followed by an outbound network connection from a common scripting or runtime environment (node, python, bun) within 60 seconds, potentially indicating credential or configuration exfiltration.
Detects behavior consistent with the Pay2Key ransomware, which checks for the presence of Avast security software directories, verifies SYSTEM account access via registry queries, and performs subsequent DLL sideloading by dropping 'powrprof.dll' into the Avast application folder.
Detects Python processes (potentially associated with the ChocoPoC RAT, slogsec, or logcrypt modules) performing file read or access operations against common browser credential store files such as Login Data, Cookies, and Web Data on Windows and macOS systems.
This rule detects attempts by common scripting or utility processes (like osascript, curl, zsh, bash, sh, python) to access sensitive macOS files, specifically targeting browser data (Chrome, Brave, Firefox) and keychain files. This activity could indicate an attacker attempting to exfiltrate credentials, session tokens, or other sensitive user data.
Detects anomalous behavior associated with msiexec.exe that aligns with activity patterns of infostealer malwares like Lumma Stealer and Amadey Bot. This includes cross-process injection from unsigned processes, spawning of msiexec.exe by LOLBins or script interpreters, suspicious outbound network connectivity, and unauthorized access to browser credential storage files.
Detects unauthorized processes attempting to access or perform operations on sensitive files such as browser credentials, SSH keys, crypto wallets, and password manager databases. The rule specifically looks for non-standard or unsigned processes interacting with a high volume of these files in a short time frame, which is indicative of credential harvesting by infostealer malware like Lumma, Redline, or Vidar.
Detects malicious browser extension activity related to the StegoAd infostealer. The rule identifies a dual-indicator pattern consisting of outbound network connections to 'mitarchive.info' combined with concurrent read access to browser-specific credential store files (Google Chrome, Microsoft Edge, Mozilla Firefox) on the same host.
This rule detects when the 'oskmenu.xml' file, located in 'C:\Program Files\Common Files\microsoft shared\ink\fsdefinitions', is accessed or modified by any process not running under the SYSTEM account. This file is associated with the On-Screen Keyboard and its modification by non-system accounts could indicate an attempt to alter its behavior, potentially for persistence or privilege escalation.
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