Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
61,043 detections
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This rule captures all successful login events to serve as the foundational data source for downstream correlation engines. It is designed to identify suspicious patterns such as credential reuse across disparate IP addresses, anomalous device fingerprints following MFA, or account logins shortly after phishing visits. This filter acts as a raw event provider for more complex logic that cannot be evaluated within a single-event context.
Detects anomalous SharePoint search activity that uses 'indexdocid' queries targeting specific site or web content classes. This pattern is often indicative of automated reconnaissance or enumeration of documents within a SharePoint environment, potentially attempting to bypass standard UI navigation to identify or access sensitive content.
This rule detects the execution of AnyDesk within a 30-minute window of Microsoft Teams activity on the same device. This pattern is indicative of a vishing attack, where a malicious actor lures a victim into installing remote access software during a fake technical support session initiated via Teams.
Detects instances where node.js or npm processes spawn child processes that subsequently perform write operations into Windows startup folders or registry run keys within a one-hour window. This behavior is indicative of a second-stage payload execution and persistence mechanism often leveraged by threat actors.
Detects network activity associated with the NPM Btree malware campaign, specifically TLS/HTTP communication with Ethereum Sepolia RPC endpoints (Alchemy and Infura) and requests containing a hardcoded malicious contract address.
This rule detects instances where the legitimate remote desktop application AnyDesk (AnyDesk.exe) acts as the parent process to spawn a Windows command shell (cmd.exe) or execute a batch script (.bat). This behavior is often indicative of unauthorized remote access or the execution of malicious payloads by adversaries leveraging remote support tools.
Detects the modification of InProcServer32 registry keys within the user's Classes/CLSID hive to point to a file located in the AppData directory. This pattern is commonly used for COM hijacking to achieve persistence or execute arbitrary code under the user's context.
This rule detects network connections or DNS requests to domains specifically structured to impersonate MFA registration, passkey setup, or SSO login pages. These domain patterns are commonly associated with phishing campaigns intended to harvest credentials, session tokens, or bypass multi-factor authentication (AiTM phishing).
This rule identifies potential automated activity by monitoring for specific HTTP User-Agents (e.g., Python requests, Microsoft Graph client) associated with file and mail access events. It groups by UserId and UserAgent to detect anomalous patterns that may indicate unauthorized data access or interaction by automated tools or scripts.
Detects the 'sharedLoad.min.js' loader script associated with the malicious 'indexed-btree' npm package family. This script utilizes X25519 ECDH for key exchange and AES-256 decryption to retrieve and execute a second-stage payload hosted on a Sepolia smart contract.
This rule correlates the receipt of a potential extortion-themed email (containing keywords related to ransom, bitcoin, or data leaks and mentioning cloud platforms like SharePoint, OneDrive, or Box) with a subsequent bulk access event of mail items by the same recipient using a specific client application identifier. This pattern is indicative of an attacker attempting to extort a user or organization following the unauthorized exfiltration or access of sensitive email data stored in cloud services.
Detects the loading of wkspbroker.exe or radcui.dll modules from the Microsoft RemoteApp Gateway directory located within the user's AppData path. This behavior may indicate an attempt to utilize or manipulate Remote Desktop components from a non-standard or user-writable location.
Detects Node.js processes executing command lines containing known malicious indicators or accessing specific suspicious JavaScript files (e.g., sharedLoad.min.js) associated with malicious loaders or hardcoded cryptographic keys.
Detects high-volume, diverse administrative and discovery activities in cloud environments, including SharePoint site enumeration, Azure AD user/identity discovery, and suspicious access to mail items via specific client applications, potentially indicating post-compromise reconnaissance or data harvesting.
Detects the execution of AnyDesk shortly after a Microsoft Teams meeting or call initiation on the same host. This pattern is commonly observed in social engineering attacks where an adversary convinces a user to use remote access software during a live support or phishing call.
This rule detects a multi-stage attack chain targeting a single user within a 2-hour window. It identifies the combination of email bombing (a distraction technique), an incoming external or anonymous Teams call (social engineering channel), and the installation of common remote access tools (e.g., AnyDesk, TeamViewer) on the same host.
Detection of suspicious network traffic directed toward Slack APIs, characterized by the use of hardcoded Slack bot tokens and specific communication patterns associated with the 'indexed-btree' npm malware family, indicating potential C2 communication or data exfiltration.
Detects Azure AD sign-in events indicative of a session token replay attack. The rule identifies successful sign-ins where the traffic source originates from known residential proxy infrastructure (anonymizing services often used by attackers) and the authentication process lacks a fresh interactive MFA challenge, suggesting the use of a stolen, valid session token.
This rule detects the use of specific tools associated with credential dumping and memory forensics, including Dokan driver installations for file system mounting, the use of DumpIt for creating memory dumps, and the subsequent analysis of these dumps using MemProcFS to access sensitive process memory information such as LSASS minidumps.
Detects the abuse of the forfiles.exe utility to indirectly execute command-line interpreters such as cmd.exe, powershell.exe, or other common LOLBins. Adversaries use this technique to bypass security controls that monitor or restrict direct execution of these interpreters.
Detects the invocation of Windows Subsystem for Linux (WSL) binaries (wsl.exe or wslconfig.exe) using command-line arguments that enable arbitrary command execution, such as -e, --exec, or shell interpreters (bash -c, sh -c), or web-based retrieval tools like curl/wget. This technique is often used to execute commands or download payloads within a Linux environment on Windows, potentially bypassing security controls focused on native Windows processes.
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