Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
956 detections
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Detects Node.js processes accessing paths associated with cryptocurrency wallets and browser extension storage (MetaMask, Phantom, Exodus, Electrum). This behavior is characteristic of infostealer malware attempting to exfiltrate seed phrases, keys, or wallet configuration data.
This rule detects potentially malicious web requests attempting to access sensitive local system files, environment variables, or application source code within a cloud-hosted application environment. The detection logic focuses on URI patterns indicative of Local File Inclusion (LFI) attempts targeting proc filesystem nodes, credential configuration files, or application source bundles.
Detects web application processes (such as nginx, gunicorn, uvicorn, python, node, or java) attempting to read sensitive information from /proc/self, specifically /proc/self/environ, /proc/self/cmdline, or /proc/self/maps. This behavior is indicative of a Local File Read (LFR) vulnerability exploitation, which an attacker may use to exfiltrate environment variables containing secrets or Cloud Run service account credentials.
Detects high-frequency file creation, modification, or renaming activity within the user's Desktop directory. This pattern is often associated with ransomware activity, automated data exfiltration tools staging files, or malicious droppers unpacking payloads.
This rule monitors for file system operations (creation, modification, deletion, or renaming) performed by a Node.js process when interacting with sensitive directories and files associated with web browser extensions and cryptocurrency wallets. This pattern is commonly observed in credential and private key theft activities where malicious scripts attempt to exfiltrate wallet data.
This rule monitors for the Node.js process (node.exe or node) accessing common sensitive configuration files, SSH keys, cloud credentials, or environment files such as .ssh/id_rsa, .aws/credentials, .npmrc, and .docker/config.json. Such activity is often indicative of an adversary attempting to perform credential theft or environment discovery after gaining initial access via a compromised Node.js application.
Detects the use of OSQL.EXE to interact with Microsoft SQL Server databases and redirect query results—such as user tables or credential data—to files on disk. This activity is consistent with data collection and staging efforts used by threat actors to exfiltrate database contents.
This rule detects unauthorized processes attempting to access sensitive web browser files (such as Login Data, Cookies, and Web Data) which store credentials, sessions, and history. It monitors file access events specifically targeting Chrome, Edge, and Firefox profiles, while excluding expected legitimate browser process names.
Detects attempts by explorer.exe to access browser credential and cookie files from common web browsers (Chrome, Edge, Firefox). This activity is highly suspicious as explorer.exe is not typically involved in legitimate access to these sensitive files, suggesting potential credential theft or reconnaissance.
Detects unauthorized processes attempting to access sensitive browser credential stores, such as Login Data, Cookies, or Local State, typically targeted by the Millenium RAT (DotStealer component) to harvest credentials and bypass Chromium App-Bound encryption.
Detects unauthorized processes reading or opening files located within the data directories of Discord or Telegram Desktop. These directories often store sensitive user data, including session tokens and local databases, which are common targets for info-stealing malware.
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 malicious process, file, and registry activities associated with Hannibal Stealer staging directories and characteristic command-line execution patterns, specifically targeting indicators like CefSharp.BrowsersSubprocess and specific DLL function delegates used in staging.
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 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.
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 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 non-Steam processes accessing Steam configuration files (e.g., config.vdf, loginusers.vdf, ssfn files) and reading Steam-related registry keys. This behavior is indicative of infostealer malware attempting to hijack Steam sessions or exfiltrate account configuration data.
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