Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where processes other than the primary web browsers (Chrome, Brave, Firefox) create, rename, or modify sensitive browser data files like 'Cookies' or 'Login Data'. This behavior is indicative of credential theft or data exfiltration attempts where an adversary is accessing browser-stored information.
Detects processes that access sensitive browser credential or cookie files (e.g., Login Data, Cookies) followed by network activity within a 10-minute window, which is a common behavior pattern of information-stealing malware such as Amatera or ACR Stealer.
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 potentially unauthorized access or attempts to copy sensitive SSH keys (e.g., id_rsa, id_ed25519) by command-line utilities (such as cat, cp, tar, scp, or curl) or when triggered by automation-related process names like 'terraform'. It monitors both process command lines and file events to identify staging or exfiltration activities.
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 execution of Coder-related processes or scripts where sensitive configuration parameters, such as database credentials or authentication tokens, are passed via command-line arguments. This behavior can lead to the exposure of credentials to other users or processes on the system through process listing.
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.
Detects NTLM network logons (NtLmSsp) for accounts that may have been targets of certificate-based identity abuse (e.g., ESC1). Attackers often leverage this technique to extract NTLM hashes from PKINIT TGT PACs and subsequently utilize Pass-the-Hash for lateral movement within the network.
Detects processes that access sensitive browser credential or cookie files (e.g., Login Data, Cookies) followed by network activity within a 10-minute window, which is a common behavior pattern of information-stealing malware such as Amatera or ACR Stealer.
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 execution of Coder-related processes or scripts where sensitive configuration parameters, such as database credentials or authentication tokens, are passed via command-line arguments. This behavior can lead to the exposure of credentials to other users or processes on the system through process listing.
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.
Detects when a Node.js process initiates a low-level keyboard hook (WH_KEYBOARD_LL or WH_KEYBOARD) while running from common user-writable directories, which may indicate malicious keylogging activity by a suspicious process.
Detects the use of the 'netsh wlan show profile' command with the 'key=clear' argument, which is used to display the plaintext passwords of stored WiFi profiles on a Windows system.
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 attempts to dump the process memory of the Local Security Authority Subsystem Service (LSASS), a common technique used by attackers to harvest credentials from memory. This includes the use of legitimate diagnostic tools like procdump and comsvcs.dll, as well as the identification of resulting dump files in directory paths associated with LSASS.
Detects anomalous access to specific registry keys under SYSTEM\CurrentControlSet\Control\Lsa that are associated with LSA secrets. The rule monitors for multiple registry operations (set, create, delete) performed by processes other than standard Windows system processes (lsass.exe, svchost.exe, winlogon.exe, services.exe), which is often indicative of credential dumping attempts.
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.
Detects coordinated behavioral signals associated with the CVE-2025-25249 campaign, which deploys the PivotC2 Node.js RAT on FortiGate devices. The rule correlates anomalous inbound CAPWAP (UDP/5246) traffic, Node.js process execution, suspicious JavaScript file writes, and unauthorized access to Fortinet configuration files, followed by long-lived outbound TLS beacons to non-standard ports.
Detects unauthorized modifications to critical authentication-related configuration files and registry keys. Specifically monitors changes to LSA security packages and notification packages on Windows systems, as well as PAM configuration file modifications on Linux systems, when performed by unsigned or untrusted processes outside of known installer activity.
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