Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
115 detections
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Detects specific user-initiated HTTP requests that contain suspicious prompts, such as requests for lists of names or email formats. This behavior is often associated with reconnaissance or data collection activities, potentially related to social engineering or phishing preparations.
This rule detects suspicious activity associated with the Cavern Manticore threat group, specifically the loading of the 'mhm.dll' module followed by the creation of archive files (.zip, .rar, .7z) within one hour by the same process. This behavior is indicative of data staging for exfiltration, potentially involving DPAPI-protected data.
Detects instances where known post-exploitation tools or techniques (AD Explorer snapshots, staging of screen captures in public directories, or credential stealer execution) are followed by successful outbound network connections from the same device within a 30-minute window. This behavior often correlates with the collection and subsequent exfiltration of sensitive information.
Detects an unusually high volume of successful operations related to Azure App Services originating from a single caller within a one-hour window. This could indicate automated activity, reconnaissance, or a misconfigured application.
Detects the creation or modification of Power Automate Flows that utilize non-Microsoft domains for HTTP or webhook connectors. Adversaries may abuse Power Automate to automate exfiltration or command-and-control tasks by connecting workflows to external malicious endpoints.
Detects bulk download of Slack messages using the API
Detects the export of Microsoft Teams messages, transcripts, recordings, and chat retrieval activities, particularly when performed via the Graph API. This rule identifies suspicious activity by looking for specific operation names in SaaS audit logs and flags potentially malicious user agents commonly associated with automation tools (e.g., python-requests, axios, go-http, curl, wget, powershell, httpx, aiohttp, node). It also categorizes user agents by risk level (HIGH, MEDIUM, NONE) and extracts relevant details like device IDs, IP addresses, and chat information. This activity can indicate post-compromise attackers using stolen tokens to silently export chat history.
Detects the creation or modification of Power Automate Flows that utilize non-Microsoft domains for HTTP or webhook connectors. Adversaries may abuse Power Automate to automate exfiltration or command-and-control tasks by connecting workflows to external malicious endpoints.
Detects execution of SharpHound (BloodHound) for Active Directory enumeration and subsequent exfiltration of collected datasets, specifically tracking activity attributed to the MexicanMafia (Operation Escaneo).
Detects suspicious access to browser credential stores (Google Chrome, Microsoft Edge, Mozilla Firefox, Brave) by non-browser processes within a short time frame (2 hours) after a FortiClient VPN logon event. This activity is consistent with information stealer malware, such as LummaC2, Phantom Stealer, Aurora Stealer, Luca Stealer, and Formbook, often observed in post-compromise phases of campaigns like FortiBleed.
Detects processes that access a threshold of five or more sensitive configuration, credential, or browser-stored login files within a single execution context, which is indicative of credential harvesting or automated collection activity.
This rule detects an unusually high number of web search queries (identified by 'search' or 'query' in the URI) originating from a single IP address within a one-hour window. A threshold of 100 or more queries triggers the alert. This behavior could indicate automated activity such as web scraping, reconnaissance, or a denial-of-service attempt against the search functionality.
This rule detects a potential data collection or exfiltration attempt in Microsoft Teams by identifying accounts that have accessed an abnormally high number of messages (more than 100 within a 5-minute window). This behavior may indicate an automated process or a compromised user account attempting to scrape internal communications.
This rule detects suspicious activity related to high-volume Server Message Block (SMB) share enumeration and the execution of a specific Python script named 'backup_dfs.py'. It combines three detection logics: identifying processes executing 'backup_dfs.py', detecting an unusual number of SMB network connections to common administrative shares (SYSVOL, NETLOGON, DFS), and flagging high-volume SMB-related identity directory events targeting SYSVOL or NETLOGON. This behavior could indicate an adversary performing discovery of network shares, collecting data, or preparing for data exfiltration.
Detects thousands of OneDrive or SharePoint file downloads by a single account during a short period, indicating automated collection rather than ordinary user activity.
Detects a non-browser process reading active browser titles, URLs, or accessibility trees and matching Brazilian bank names before activating fraud modules. Specifically, it looks for processes named 'banana', 'client', 'update', or 'service' (case-insensitive) that are executing commands related to UI automation or window title retrieval, and simultaneously searching for banking-related keywords like 'bank', 'internet banking', 'agencia', or 'conta'.
This rule detects a potential data collection or exfiltration attempt in Microsoft Teams by identifying accounts that have accessed an abnormally high number of messages (more than 100 within a 5-minute window). This behavior may indicate an automated process or a compromised user account attempting to scrape internal communications.
This rule detects a potential data collection or exfiltration attempt in Microsoft Teams by identifying accounts that have accessed an abnormally high number of messages (more than 100 within a 5-minute window). This behavior may indicate an automated process or a compromised user account attempting to scrape internal communications.
This rule detects suspicious activity related to high-volume Server Message Block (SMB) share enumeration and the execution of a specific Python script named 'backup_dfs.py'. It combines three detection logics: identifying processes executing 'backup_dfs.py', detecting an unusual number of SMB network connections to common administrative shares (SYSVOL, NETLOGON, DFS), and flagging high-volume SMB-related identity directory events targeting SYSVOL or NETLOGON. This behavior could indicate an adversary performing discovery of network shares, collecting data, or preparing for data exfiltration.
Detects thousands of OneDrive or SharePoint file downloads by a single account during a short period, indicating automated collection rather than ordinary user activity.
Detects the execution of Impacket tools from a specific portable bundle path ('mini_imp/') often utilized by the threat actor MexicanMafia during 'Operation Escaneo'. The activity involves Active Directory enumeration and reconnaissance of service accounts, including those associated with SQL services, Citrix administrators, and CyberArk vault operators.
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