Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential credential harvesting attempts by identifying emails that contain keywords commonly associated with phishing (e.g., 'password', 'update', 'verify' in the subject) and URLs that suggest login or account pages (e.g., 'form', 'login', 'account'). It aggregates these events from the same sender over a one-hour period and flags senders with three or more such emails.
This rule detects suspicious activity related to Azure Active Directory service principals. It specifically looks for a service principal performing multiple (5 or more within an hour) 'Add service principal', 'Add owner to application', or 'Add permission grant' operations. This pattern could indicate an attacker attempting to escalate privileges or establish persistence within an Azure AD environment by manipulating service principals and application permissions.
Detects suspicious file creation activities by system authentication processes (e.g., sshd, su, sudo, PAM) in known staging directories (/tmp, /var/tmp, /dev/shm) or SSH configuration paths. The rule looks for hidden files or files with specific extensions (.log, .dat, .bak) often associated with malicious backdoored components used to intercept and store credentials in plaintext.
Detects the execution of known Kerberoasting tools such as Rubeus and Impacket (GetST, GetTGT, asktgs) or command-line arguments indicative of Kerberoasting, specifically targeting RC4/ETYPE-23 ticket requests that are often used to crack service account passwords offline.
This rule detects unauthorized attempts to create, modify, rename, or delete sensitive browser data files (such as Login Data, Cookies, key4.db, and logins.json) used for storing credentials. It monitors for access by processes that are not known browser executables or authorized system services, which is a common indicator of credential dumping by malicious software.
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 the execution of Mimikatz specifically utilizing the lsadump::dcsync command or the internal DRSGetNCChanges function call. These behaviors are indicative of an adversary performing directory replication abuse against a Domain Controller to extract sensitive Active Directory credentials, including password hashes.
Detects unauthorized processes attempting to read, modify, create, or rename browser sensitive data files such as 'Login Data' (passwords) and 'Cookies' for Google Chrome and Microsoft Edge. This activity is a common indicator of credential theft or session hijacking.
Detects instances where Chrome or Microsoft Edge web browsers access local sensitive credential storage files (e.g., Login Data, Web Data, Cookies) from unexpected file system paths, combined with external network traffic from the same process instance. This behavior is indicative of potential malicious browser extension abuse, DLL sideloading, or unauthorized browser instance execution for credential exfiltration.
This rule detects potential automated reconnaissance or credential harvesting by monitoring for high volumes of LDAP queries targeting sensitive Active Directory attributes. It correlates both LDAP query events and Windows Security Event ID 4662 to identify bulk access to attributes like 'samAccountName', 'userPassword', and 'memberOf'.
This rule detects potential Golden Ticket attacks by looking for multiple successful Kerberos authentication events (EventID 4624) with LogonType 3 (network logon) and a specific TicketEncryptionType (0x17), originating from the same IP address and targeting the same user, within a short timeframe and with a long ticket lifetime. A Golden Ticket attack involves an adversary forging Kerberos Ticket Granting Tickets (TGTs) to gain unauthorized access to resources.
Detects the presence of the 'x-ms-RefreshTokenCredential' cookie in requests directed to 'login.microsoftonline.com' originating from a client that does not identify as Windows. This behavioral pattern is associated with session hijacking techniques where an attacker uses a tool (e.g., Device Code Lab) to inject a session cookie into a browser, allowing for unauthorized access to Microsoft 365 services.
Detects the creation or connection to a named pipe containing the string 'ChromeAppBoundVIP', a behavior associated with the WEES stealer malware for extracting sensitive information from web browsers.
Detects the execution of cmdkey.exe with the /list flag, which is used to enumerate credentials stored in the Windows Credential Manager. This technique is often used by adversaries for post-compromise reconnaissance to identify stored credentials for lateral movement.
Detects the execution of processes that display a fake password prompt masquerading as system utilities like Maccy or Finder. This behavior is indicative of credential phishing or a social engineering attempt to steal user credentials by prompting for a password under the guise of a system update or configuration change.
Detects unauthorized processes, such as PyInstaller binaries or Python scripts, attempting to read LevelDB storage files used by popular messaging applications (Discord, Telegram, Signal, Slack, and Microsoft Teams) for session and authentication token extraction, a technique commonly associated with information stealers like Yuta or Glove Stealer.
Detects instances where non-browser or system maintenance processes attempt to access browser credential storage files, such as 'Login Data', 'key4.db', or 'logins.json'. The rule uses a self-join to correlate access to these sensitive files with access to the Windows DPAPI protection directories, often used to decrypt these credentials.
This rule detects suspicious activity related to the Netlogon service or the LSASS process. It triggers on Netlogon service events (5827, 5828, 5829) which may indicate insecure channel usage or service issues often associated with exploitation, and on Sysmon Event ID 1 where a process is spawned by lsass.exe under the SYSTEM user, a behavior frequently seen during credential dumping or privilege escalation attempts.
This rule detects common methods used by adversaries to perform credential dumping by accessing the memory of the Local Security Authority Subsystem Service (LSASS). It specifically flags the use of Procdump to create a memory dump of LSASS, the use of the native Windows utility comsvcs.dll via rundll32 to dump process memory, and the creation of LSASS dump files via the Windows Task Manager.
Detects Node.js processes accessing sensitive configuration and credential files such as AWS credentials, .npmrc tokens, SSH keys, and Docker configurations. This behavior is consistent with infostealer activity, including malicious supply chain attacks designed to harvest developer secrets from local filesystems.
This rule detects the execution of PowerShell commands and scripts known to be used for Kerberoasting, a technique for obtaining TGS tickets that can be cracked offline to recover service account passwords. The rule matches specific tool names and command line flags associated with common offensive security tools.
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