Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
4,308 detections
Filters
Last updated
All Time
Detection languages
2,033
733
398
298
243
Contributors
1,048
740
503
372
239
Categories
20,443
11,831
5,875
5,151
4,864
4,308
Platforms
2,570
792
615
382
319
Products / Services
767
739
659
512
348
MITRE Techniques
2,347
917
847
814
635
CVEs
9
6
6
6
5
IDS Classtypes
81
66
44
14
14
IDS Protocols
136
79
11
7
4
This rule detects potential adversary-in-the-middle (AiTM) session hijacking by monitoring non-interactive sign-in events. It identifies when a refresh token is redeemed from a device identifier that does not exist in the user's recent interactive sign-in history and from a geographical location different from the user's last known interactive session.
Detects unauthorized access to or generation of Citrix NetScaler core dump files, trace outputs, or memory diagnostic artifacts. These artifacts are high-value targets for attackers, such as those exploiting CVE-2026-88772, who attempt to extract session tokens, credentials, and sensitive data residing in memory via these diagnostic mechanisms.
Detects a potential post-compromise lateral movement sequence where a host, previously identified as accessing sensitive browser credential files by an unauthorized process, subsequently initiates RDP or WinRM connections to other internal hosts using an account not previously observed performing interactive logons on that source host.
Detects instances where a Microsoft IIS worker process (w3wp.exe) executes commands such as 'findstr', 'Get-Content', or 'Select-Xml' to scan configuration files (e.g., applicationHost.config, web.config) or source code files (e.g., App_Code/*.cs) for sensitive information like passwords, connection strings, or API keys. This behavior is indicative of credential harvesting by an attacker using a webshell or compromised IIS process.
Detects unauthorized processes accessing sensitive credential storage files (e.g., 'Login Data', 'Cookies', 'Local State') from known Chromium-based browser profile directories. This activity is indicative of credential harvesting by infostealer malware attempting to exfiltrate browser data before performing decryption.
Detects a sequence of authentication activity indicative of a credential stuffing attack. The rule identifies a burst of failed password-only authentication attempts followed by a successful authentication for the same account. It requires that the IP address used for the successful login overlaps with the set of IPs that initiated the failed attempts, effectively filtering out typical user noise and highlighting potentially malicious activity where an attacker has identified a valid credential.
Detects a sequence of authentication activity indicative of a credential stuffing attack. The rule identifies a burst of failed password-only authentication attempts followed by a successful authentication for the same account. It requires that the IP address used for the successful login overlaps with the set of IPs that initiated the failed attempts, effectively filtering out typical user noise and highlighting potentially malicious activity where an attacker has identified a valid credential.
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, reading, or downloading of sensitive NetScaler system files, including configuration files (ns.conf), licensing files, and cryptographic material (private keys/certificates). This activity often indicates post-exploitation reconnaissance, staging, or data theft attempts following initial access.
Detects attempts by potentially malicious processes, such as web-browsing tools, scripts, or agents, to access the cloud instance metadata service (169.254.169.254) or associated metadata paths. This behavior is indicative of Server-Side Request Forgery (SSRF) exploitation aimed at stealing cloud instance credentials.
The following analytic identifies a possible non-common browser process accessing its browser user data profile.
This tactic/technique has been observed in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive browser information and credentials as part of their exfiltration strategy.
Detecting this anomaly can serve as a valuable pivot for identifying processes that access lists of browser user data profiles unexpectedly.
This detection uses a lookup file `browser_app_list` that maintains a list of well known browser applications and the browser paths that are allowed to access the browser user data profiles.
This tactic/technique has been observed in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive browser information and credentials as part of their exfiltration strategy.
Detecting this anomaly can serve as a valuable pivot for identifying processes that access lists of browser user data profiles unexpectedly.
This detection uses a lookup file `browser_app_list` that maintains a list of well known browser applications and the browser paths that are allowed to access the browser user data profiles.
Detects unauthorized processes attempting to read sensitive session files or databases associated with messaging applications like Telegram, Signal, and Discord. Accessing these files (such as 'tdata', 'db.sqlite', or 'leveldb') allows for full account takeover by extracting session tokens, enabling attackers to bypass authentication and 2FA.
Detects unauthorized access to DPAPI master key files located in the Windows user profile (%APPDATA%\Microsoft\Protect\). This activity is commonly associated with infostealers attempting to decrypt browser-stored credentials (cookies, passwords) by manually accessing the master key files after exfiltrating encrypted data stores.
Detects the execution of known browser credential harvesting tools (WebBrowserPassView, WebBrowserBookmarksView, or WS_Password variants) from ScreenConnect temporary directories. This behavior often indicates post-compromise activity where an adversary uses an existing Remote Monitoring and Management (RMM) session to extract credentials from browsers.
Detects repeated SSH authentication failures originating from an external source on port 22, where the username pattern appears to be programmatically generated or enumerated (e.g., username followed by digits). This behavior is characteristic of automated brute-force attempts targeting common SSH services.
Detects a sequence of authentication activity indicative of a credential stuffing attack. The rule identifies a burst of failed password-only authentication attempts followed by a successful authentication for the same account. It requires that the IP address used for the successful login overlaps with the set of IPs that initiated the failed attempts, effectively filtering out typical user noise and highlighting potentially malicious activity where an attacker has identified a valid credential.
Detects techniques commonly used to dump Active Directory domain credentials by accessing the NTDS.dit database file. This includes the use of ntdsutil for IFM (Install From Media) operations, manual shadow copy creation using vssadmin or wmic to copy the ntds.dit file, direct access to shadow copy volumes containing sensitive database files, and the use of credential dumping tools like Impacket's secretsdump.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
Detects the addition of passwords or certificates to Entra ID applications or service principals by an identity that is not currently listed as an owner. This behavior is a common persistence technique used by threat actors to maintain long-term access, bypass password resets, and circumvent MFA.
Detects the creation of a new application secret or credential in a cloud environment followed by an immediate authentication attempt using that credential from an IP address that has not been observed performing successful authentication within the specified lookback window. This behavior is indicative of potential unauthorized persistence or the use of compromised credentials by an adversary.
This rule detects when a new password credential (client secret) is added to an existing Azure AD Service Principal or application. This activity is a common method for adversaries to establish persistence in a cloud environment that survives password resets or MFA enforcement on the original administrative account.
Page 7 of 206