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
Detects a privilege escalation technique where an attacker with application management permissions adds a credential to the 'Office 365 Exchange Online' service principal (SP). The attacker then leverages the SP's 'Domain.ReadWrite.All' permission to add or modify a federated domain, allowing them to forge tokens for any hybrid user.
Detects several potentially malicious activities related to Active Directory Certificate Services (AD CS). This includes high volumes of failed requests (potential enumeration), requests for sensitive templates (privilege escalation), and potential impersonation attempts using Subject Alternative Names (SANs). These activities are associated with AD CS abuse techniques.
Detects processes other than the official Telegram desktop client accessing sensitive local data files ('key_datas', 'settingss', 'usertag') within the Telegram application directory. This activity is often associated with credential theft or session hijacking.
This rule detects file access, creation, modification, or renaming operations involving sensitive files associated with cryptocurrency wallets (Bitcoin, Electrum, ElectrumSV), game account configurations (Battle.net, Steam, Minecraft), and browser-based cookies (Roblox). The rule filters out legitimate process interactions, identifying suspicious activity from unknown or unauthorized applications potentially attempting to exfiltrate credentials or session tokens.
This rule detects the use of command-line text-processing utilities (grep, awk, sed, etc.) to search for sensitive credential-related strings (e.g., secret, token, password) within environment files or process environments. This pattern is indicative of an adversary attempting to manually extract stored credentials from configuration files or system environment variables.
This rule detects potential credential and keystroke theft by identifying the installation of a Windows hook (SetWindowsHookEx) followed within 30 minutes by file access to sensitive browser credential stores (Login Data, key4.db, etc.) by the same process on a single device.
This rule detects attempts to exploit the LiteLLM proxy pass-through endpoint to interact with the cloud Instance Metadata Service (IMDS). Specifically, it identifies configuration attempts to route traffic to the IMDS IP (169.254.169.254) and subsequent requests to retrieve IAM security credentials, which is indicative of a Server-Side Request Forgery (SSRF) attack to steal cloud identity tokens.
Detects a privilege escalation technique where an attacker with application management permissions adds a credential to the 'Office 365 Exchange Online' service principal (SP). The attacker then leverages the SP's 'Domain.ReadWrite.All' permission to add or modify a federated domain, allowing them to forge tokens for any hybrid user.
Detects instances where the ESET Remote Administrator Agent (ERAAgent.exe) terminates shortly after loading the Data Protection API service (dpapisvc.dll). This pattern may indicate an attempt to interact with or disrupt DPAPI services, potentially to facilitate credential dumping or sensitive data access.
Detects anomalous reuse of an authenticated Microsoft 365 session cookie shortly after a successful MFA-satisfied sign-in. The detection identifies scenarios where the subsequent access originates from a different IP address, device, or user agent without an intervening MFA challenge, which is a pattern indicative of session hijacking techniques like Adversary-in-the-Middle (AiTM) or session token replay.
This rule detects potential unauthenticated access or unauthorized configuration modifications to an AI agent management backend. It monitors HTTP logs for requests targeting configuration endpoints that lack proper authentication, specifically identifying attempts to retrieve sensitive information like Dify or Coze keys, or attempts to inject new agent configurations via unauthenticated POST requests.
Detects unauthorized processes (e.g., cmd.exe, powershell.exe, node.exe) accessing sensitive web browser files like 'Login Data' or 'Cookies' in common browser directories. This behavior is indicative of credential theft or data exfiltration attempts.
Detects Kerberos TGT requests (Event ID 4768) that utilize RC4 (PreAuthType 23) for pre-authentication. This is a common indicator of an Overpass-the-Hash attack, where an adversary uses a stolen NTLM hash to authenticate as a user and request a Kerberos ticket in environments that are typically configured for AES encryption.
This rule detects unauthorized or suspicious access to sensitive configuration and credential files (e.g., .aws, .azure, .ssh) by specific DLP (Data Loss Prevention) or automation scripts, and identifies subsequent attempts to expose environment variables or sensitive tokens within process command lines.
This rule detects unauthorized or suspicious access to sensitive configuration and credential files (e.g., .aws, .azure, .ssh) by specific DLP (Data Loss Prevention) or automation scripts, and identifies subsequent attempts to expose environment variables or sensitive tokens within process command lines.
Detects the creation of new AWS access keys or login profiles for a target identity immediately following the deletion or modification of a credential for the same identity, where the actors performing the two actions are different. This pattern indicates an adversary creating persistent access after a security incident responder or automated system has revoked a previously compromised credential.
Detects the creation of new AWS access keys or login profiles for a target identity immediately following the deletion or modification of a credential for the same identity, where the actors performing the two actions are different. This pattern indicates an adversary creating persistent access after a security incident responder or automated system has revoked a previously compromised credential.
Detects the creation of new AWS access keys or login profiles for a target identity immediately following the deletion or modification of a credential for the same identity, where the actors performing the two actions are different. This pattern indicates an adversary creating persistent access after a security incident responder or automated system has revoked a previously compromised credential.
Detects high-frequency creation and revocation of service principal credentials, indicating potential malicious credential cycling, or unusual bursts of service account JWT/refresh token rotation across multiple IP addresses, which may indicate an attempt to evade detection or maintain persistence.
Detects anomalous authentication attempts where a single source IP validates a high volume of distinct Service Principal or App Client IDs within a short timeframe with a high failure rate. This pattern is indicative of attackers performing bulk validation of stolen API keys or OAuth credentials, likely harvested through mobile application decompilation (APK/IPA analysis).
Detects non-interactive sign-in events involving OIDC or external authentication tokens shortly after a known Coder registry compromise. The rule flags high-risk or at-risk single-factor authentication sessions, which may indicate the replay of stolen tokens harvested during the compromise window.
Page 57 of 206