Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects unauthorized processes attempting to access Discord's local storage (LevelDB) where authentication tokens are stored, followed by a network request to the Discord API user validation endpoint, which is a pattern indicative of token extraction and liveness testing by malware.
This rule detects the use of the macOS 'security' utility to unlock the login keychain and modify its access-control list, a technique observed in PamStealer malware to facilitate subsequent unauthorized access to the keychain database.
This rule detects non-standard processes attempting to access sensitive web browser files (e.g., 'Login Data', 'Cookies', 'key4.db') that are typically only accessed by legitimate web browsers. It excludes known browser processes and trusted security or sync applications to identify potential credential theft attempts.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
Detects the loading of the 'WbElevation.dll' module associated with SectopRAT, followed by suspicious access to browser, email, or cryptocurrency wallet credential stores within a 10-minute window on the same device.
Detects the execution of known credential harvesting tools such as Mimikatz, LaZagne, and pypykatz, or the use of their specific command-line arguments. The rule includes exclusions for common security software processes and authorized testing paths.
Detects the execution of known credential harvesting tools such as Mimikatz, LaZagne, and pypykatz, or the use of their specific command-line arguments. The rule includes exclusions for common security software processes and authorized testing paths.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects anomalous authentication behavior where the same session token or identifier is utilized by multiple distinct IP addresses or User-Agent strings within a short timeframe. This behavior is indicative of Adversary-in-the-Middle (AiTM) attacks, such as those employing Evilginx, where an attacker intercepts and replays stolen session cookies to bypass standard authentication and MFA controls.
Detects potential vishing or account takeover precursor activity where an external or guest Microsoft Teams user, impersonating IT or Help Desk support, communicates with a user who subsequently modifies their credentials or MFA configuration within the same 24-hour period.
Detects instances of common interpreter processes, specifically node.exe or python variants, accessing known cryptocurrency wallet files or mnemonic storage files on a Windows system. This behavior is highly indicative of malicious activity where an attacker uses lightweight script-based tools to perform credential harvesting or theft of cryptocurrency assets.
This rule detects potential credential theft by correlating access to local git credential files (e.g., .git-credentials, .netrc) followed by a git push event from the same user account within 24 hours. The rule flags these pushes if they originate from an unrecognized host or an IP address outside of the trusted internal network range.
The detection rule monitors for access to files commonly used to store sensitive information, including .git-credentials, .netrc, and .gitconfig files configured to store credentials. Accessing these files is a common indicator of credential theft attempts by adversaries looking for plaintext authentication data to pivot to other systems or services.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
Detects unauthorized access to common browser credential and cookie files by processes other than standard web browsers. This activity is a common indicator of credential harvesting malware or unauthorized data collection.
Detects potential path traversal or unauthorized access attempts by Semantic Kernel Agents (or related processes like Python/DotNet) to sensitive host files. The rule monitors for file read, creation, or modification activities targeting sensitive credentials, configuration files (e.g., .ssh, .aws, .kube), and system files, which could indicate a sandbox escape or malicious data collection.
Detects unauthorized processes (non-browser) reading sensitive browser session and credential files (e.g., Cookies, Login Data) across multiple user profiles, followed by immediate outbound network connections. This behavior is highly characteristic of commodity information stealers (e.g., LummaC2, Stealc, Vidar) attempting to exfiltrate browser-stored credentials and session data.
Detects instances where non-browser processes (e.g., cmd, powershell, or malicious binaries) attempt to access sensitive browser files such as 'Login Data' or 'Local State'. This is a common technique used by credential stealers to exfiltrate stored browser passwords, cookies, and configuration data.
Detects outbound network connections (HTTP/TLS) from internal hosts to a known malicious IP address associated with Adversary-in-the-Middle (AiTM) phishing infrastructure, specifically targeting Microsoft 365 credential and session cookie theft.
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