Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors for security alerts related to Azure AI services, specifically detecting credential theft attempts and LLM jailbreak attempts that have been blocked or detected by Azure's built-in content filtering mechanisms.
Detects attempts to abuse the device code flow targeting Microsoft Office and Authentication Broker applications from unmanaged, non-compliant devices with unbound tokens. This activity is indicative of device code phishing, where an attacker tricks a user into authenticating a malicious application.
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 rapid, automated enumeration of Azure AD users, groups, and directory roles via the Microsoft Graph API immediately post-compromise. This rule identifies a high volume of queries (more than 50 within a 5-minute window) originating from a single account and IP address, targeting either 'Microsoft Graph' as the application or 'SearchQueryPerformed' as the action type.
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-interactive Microsoft Graph OAuth2 token requests originating from unmanaged, deviceless sessions that falsely claim MFA satisfaction. The detection focuses on specific user agents known to be associated with automated token replay attacks and looks for multiple unique token identifiers per user session, which is indicative of credential or token theft rather than standard service account or application activity.
Detects attempts to dump the memory of the Local Security Authority Subsystem Service (LSASS) process using the built-in Windows comsvcs.dll via rundll32.exe or tasklist commands, a common technique used for credential harvesting.
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.
Detects network communication to the malicious domain 'mitarchive.info' from common web browsers (Chrome, Edge, Firefox, Brave). This domain is associated with the StegoAd browser extension, which intercepts Google and WordPress login flows to exfiltrate stolen credentials and 2FA codes.
This rule detects suspicious activity related to accessing or dumping the NTDS.dit file, which contains Active Directory credential information. It looks for process creation events involving tools commonly used for NTDS.dit extraction (e.g., ntdsutil, secretsdump), file access events on NTDS.dit paths, and privilege use (SeBackupPrivilege, SeRestorePrivilege, SeDebugPrivilege) often associated with credential dumping.
This rule detects multiple failed logon attempts (more than 5 within a 5-minute window) for user accounts from a single IP address. This behavior is indicative of a brute-force attack or password guessing attempt against user accounts.
This rule detects instances where a user clicks on a URL identified as potentially malicious (phishing-related) and subsequently has a risky sign-in event in Azure AD. The rule correlates URL click events from email with Azure AD sign-in logs, specifically looking for sign-ins marked as risky or originating from suspicious user agents (e.g., specific axios versions). The phishing URLs are identified by keywords such as 'azureapplicationregistration.pages.dev', 'chrnobinson.com', 'workers.dev', '.pages.dev', '.web.core.windows.net', '/oauth2/v2.0/authorize', 'client_id=', and 'scope=openid'.
This rule detects Adversary-in-the-Middle (AiTM) session hijacking against Microsoft 365, specifically targeting the Tycoon 2FA phishing kit (associated with campaigns like Payroll Pirate). It correlates two distinct malicious behaviors: the initial MFA capture phase (involving specific error codes and spoofed user agents) and subsequent illicit token replay against the Microsoft Graph API from unmanaged devices.
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.
Detects unauthorized usage of the 'osascript' command to interact with the 'security' binary on macOS, specifically targeting keychain operations such as dumping, finding generic passwords, or unlocking the login keychain. This behavior is indicative of Meow RAT attempting to exfiltrate credentials from the macOS Keychain or browser-stored secrets.
Detects unauthorized attempts to enumerate stored Windows credentials using native command-line utilities such as vaultcmd.exe and cmdkey.exe, or by invoking CredEnumerate/Get-StoredCredential functions via PowerShell scripts. These methods are commonly used by attackers to gain access to cached passwords, tokens, or network credentials stored in the Windows Credential Manager.
Detects unauthorized processes (those not identified as standard web browsers) accessing browser profile directories (Local Storage or IndexedDB) associated with major AI platforms such as OpenAI, ChatGPT, Anthropic, and Copilot. This behavior is indicative of credential theft or session token exfiltration from local browser storage.
Detects unauthorized processes accessing sensitive Google Chrome credential files (Login Data and Cookies) typically stored in the AppData local folder. This behavior is characteristic of information stealers and trojans like ModeloRAT, Backdoor.Mistic, StealC, and Amadey, which attempt to exfiltrate stored browser passwords and session cookies.
This rule monitors for security alerts related to Azure AI services, specifically detecting credential theft attempts and LLM jailbreak attempts that have been blocked or detected by Azure's built-in content filtering mechanisms.
This rule detects when processes other than known web browsers (Chrome, Firefox, Edge, Opera) attempt to access sensitive browser-related files such as 'Login Data', 'Cookies', and database files. It specifically flags instances where the accessing process or its parent process originates from 'C:\ProgramData' or where the parent process is 'powershell.exe', which is a common pattern for malicious credential access tools.
Detects ROPC (Resource Owner Password Credentials) token requests to Microsoft Graph using specific client IDs associated with Microsoft To-Do or Intune Company Portal. This activity could indicate an attempt to bypass MFA via hidden Conditional Access policy exclusions, as described in CVE-2025-0309.
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