Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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IDS Classtypes
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Detects the reuse of a session or refresh token from a geographic location that contradicts recent user activity (impossible travel) without a new interactive MFA challenge. This activity is indicative of Adversary-in-the-Middle (AiTM) phishing attacks, such as those leveraging Evilginx, where session cookies are captured and replayed from attacker infrastructure to bypass multi-factor authentication.
Detects successful remote authentication events (Network or Interactive) performed by AI agent orchestration service accounts. This rule monitors for atypical lateral movement patterns where an automated service account pivots to remote hosts using protocols such as RDP or WinRM, diverging from its standard operational context.
Detects multiple Kerberos AS-REQ (Event ID 4768) requests initiated for distinct user accounts from a single source IP within a short timeframe (10 minutes) where Kerberos pre-authentication was not used (Pre-Authentication Type 0). This pattern is a key indicator of AS-REP roasting activity, often performed by tools like Rubeus or Impacket's GetNPUsers.py to harvest user TGTs for offline cracking.
Detects a single user account performing NTLM network logons (LogonType 3) to 3 or more distinct hosts within a short time period. This pattern is commonly associated with lateral movement techniques such as Pass-the-Hash, where an attacker uses compromised credentials to spread across a network.
This rule monitors for suspicious activity related to AI service accounts and API keys. It detects potential account or key compromise by identifying impossible travel sign-ins coupled with AI API usage, simultaneous usage of a single API key from multiple disjoint network locations, and sudden, high-volume activity from previously dormant service account API keys.
This rule detects successful user logins from multiple geographical locations (impossible travel) where multi-factor authentication (MFA) was not utilized. This behavior is indicative of credential theft or account takeover attempts.
Detects potential exploitation of Active Directory Certificate Services (AD CS) misconfigurations (specifically ESC1, involving client-auth EKU and enrollee-supplies-subject). The rule correlates certificate issuance events (4886, 4887, 4888) involving sensitive templates or suspicious subject alternative name (SAN) mismatches with subsequent Kerberos PKINIT authentication (4768) events using the issued certificate.
Detects anomalous authentication behavior where the same session token or identifier is utilized by multiple distinct IP addresses or User-Agent strings within a short timeframe. This behavior is indicative of Adversary-in-the-Middle (AiTM) attacks, such as those employing Evilginx, where an attacker intercepts and replays stolen session cookies to bypass standard authentication and MFA controls.
Detects a successful login attempt by the user account 'p2pwn' targeting port 37777. Port 37777 is commonly associated with DVR/NVR surveillance equipment and is frequently targeted by brute-force attacks and botnets attempting to gain unauthorized access to embedded devices.
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
Detects Microsoft Entra ID sign-in events where a user authenticates from a known residential proxy ASN, matching the user's historical geography but using a new, unrecognized device fingerprint. This behavior is indicative of session token replay attacks, where an adversary uses stolen session cookies to bypass standard authentication.
Detects a successful login attempt by the user account 'p2pwn' targeting port 37777. Port 37777 is commonly associated with DVR/NVR surveillance equipment and is frequently targeted by brute-force attacks and botnets attempting to gain unauthorized access to embedded devices.
Detects the reuse of session identifiers (SessionId, DeviceId, or RefreshTokenJti) across different device fingerprints (e.g., changes in User-Agent, OS, or Browser) within a short timeframe. This behavior is indicative of session hijacking or adversary-in-the-middle (AiTM) attacks, where stolen session tokens or cookies are replayed by an attacker from a different endpoint than the original user.
Detects instances where a user account registers a new Multi-Factor Authentication (MFA) method (such as TOTP or Authenticator app) shortly (within 30 minutes) after a high-risk or potentially anomalous sign-in event. This pattern is commonly associated with adversary-in-the-middle (AiTM) phishing attacks where an attacker captures an authenticated session and uses it to enroll a secondary, attacker-controlled MFA device for persistent account access.
Detects a potential post-exploitation attack chain where a user receives a phishing lure and subsequently executes a rapid burst of distinct process commands on the same host. This pattern is consistent with an adversary utilizing an LLM to iteratively troubleshoot and refine command syntax during a post-exploitation phase.
Detects a potential post-exploitation attack chain where a user receives a phishing lure and subsequently executes a rapid burst of distinct process commands on the same host. This pattern is consistent with an adversary utilizing an LLM to iteratively troubleshoot and refine command syntax during a post-exploitation phase.
Detects a potential post-exploitation attack chain where a user receives a phishing lure and subsequently executes a rapid burst of distinct process commands on the same host. This pattern is consistent with an adversary utilizing an LLM to iteratively troubleshoot and refine command syntax during a post-exploitation phase.
Detects high-frequency failed RDP authentication attempts (Windows Event ID 4625 with LogonType 10) originating from the same source. This pattern is indicative of brute-force or credential guessing attacks against Remote Desktop Services.
Detects potential lateral movement or pivot attempts by correlating repeated failed RDP logons (RemoteInteractive) against sensitive targets (Domain Controllers, File Servers, Backup Servers) with concurrent security tool blocks (e.g., Microsoft Defender Antivirus, Exploit Guard) on the originating beachhead device within a 4-hour window.
Detects a potential TeamFiltration-style compromise where multiple accounts with no history of successful authentication and no MFA enforced authenticate successfully within a 7-minute burst window. This behavior is indicative of an adversary mass-testing or accessing newly compromised dormant or un-secured service accounts.
Detects potential credential reuse attacks such as Pass-the-Hash, Pass-the-Ticket, or Overpass-the-Hash. The rule identifies a 'NewCredentials' logon (e.g., runas /netonly) followed by a network or remote interactive logon to a target system (such as a domain controller) by the same user, without an interactive logon session occurring between the events, which is indicative of using stolen credentials to move laterally.
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