Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potentially malicious software supply chain attacks by identifying suspicious child processes spawned by package managers (pip, npm) or Python scripting engines within a short timeframe. It specifically looks for shells (cmd, powershell, bash, etc.) being invoked immediately after a package installation or script execution, and correlates this activity with suspicious outbound network connections originating from these processes.
This rule detects potential Business Email Compromise (BEC) attempts by identifying emails containing financial keywords sent to sensitive finance/accounting roles from domains that have not been observed in the last 180 days. It then correlates this email event with subsequent device-level network activity involving banking or financial URLs initiated by the recipient user, which may indicate follow-up reconnaissance or unauthorized access.
Detects potential financial data exfiltration by monitoring for bulk downloads of files with financial keywords from SaaS/Office365 platforms, and identifies unauthorized processes attempting to access sensitive financial application data files (.qbw, .qbb, etc.) on local systems.
Detects exploitation attempts against Flowise by leveraging a case-sensitivity bypass in environment variable parsing. Attackers inject variants like 'node_options' into the environment, which bypasses security denylists, allowing the 'NODE_OPTIONS' variable to be set. This enables remote code execution (RCE) via the '--require' flag in child Node.js processes, alongside subsequent activities like marker file creation, registry-based persistence, or unauthorized outbound network connections from the Flowise service.
This rule detects outbound network connections to a specific typosquatted domain 'perplexity-ai.online'. This domain is associated with a malicious browser extension designed to intercept and exfiltrate search queries before forwarding them to legitimate search providers.
This rule detects network activity and email signals associated with a malicious campaign promoting a fraudulent 'Perplexity AI' Chrome extension. It monitors for connections to known malicious Tilda landing pages, correlates these visits with subsequent Google Chrome extension (CRX) downloads within a 10-minute window, and identifies phishing emails containing links to these malicious pages.
Detects anomalous activity associated with information stealing malware, including unauthorized access to browser credential files (Login Data, Cookies), remote thread injection into browser processes, outbound connections to the Telegram API by non-browser processes, and rapid bulk access to multiple sensitive credential or crypto wallet files.
This rule detects potential command and control (C2) beaconing activity by identifying devices communicating with known suspicious remote IP addresses at regular, repeating intervals. It calculates the time difference between consecutive connections to specific suspicious IPs and identifies devices that establish at least three connections within one-hour bins, adhering to a 1 to 15-minute heartbeat interval.
This rule detects potential Business Email Compromise (BEC) attempts by identifying emails containing financial keywords sent to sensitive finance/accounting roles from domains that have not been observed in the last 180 days. It then correlates this email event with subsequent device-level network activity involving banking or financial URLs initiated by the recipient user, which may indicate follow-up reconnaissance or unauthorized access.
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 creation or update of an Azure Sentinel analytics rule. This can indicate administrative activity related to security monitoring configuration. Monitoring these changes is crucial for detecting potential tampering with detection capabilities or the introduction of malicious rules.
Detects potential Command and Control (C2) communication associated with Emotet malware by monitoring for successful network connections to suspicious remote ports frequently used by the malware. The rule flags endpoints that establish connections to at least three of these high-risk ports within a one-hour window, which is indicative of automated C2 beaconing behavior.
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 anomalous network communication patterns and file system changes indicative of SoftEther VPN usage, particularly when linked to processes masquerading as VMware or communicating with known malicious C2 infrastructure. The rule monitors for network connections on common SoftEther ports, connections to specific command-and-control IP addresses, and the creation of SoftEther configuration files like 'hamcore.se2' or 'vpn_bridge.config'.
Detects outbound network connections to a known KuinaExtractor (k0to) infostealer C2 server. The rule monitors DeviceNetworkEvents for connections to the malicious IP address 103.229.53.18 on port 3000 and correlates them with process information to identify the originating application.
Detects DNS queries and network connection attempts to a set of known malicious domains associated with the threat actor UNC1151 (Ghostwriter). This rule monitors both DNS resolutions and direct network connection attempts to these indicators of compromise (IOCs), which are commonly used in spearphishing campaigns.
Detects outbound network connections to a known KuinaExtractor (k0to) infostealer C2 server. The rule monitors DeviceNetworkEvents for connections to the malicious IP address 103.229.53.18 on port 3000 and correlates them with process information to identify the originating application.
Detects GitHub Actions runner processes (e.g., node, bash, python) accessing sensitive CI/CD environment variables (GITHUB_TOKEN, NPM_TOKEN) followed within 30 seconds by an outbound network connection to a non-GitHub domain. This pattern is indicative of potential secret exfiltration by compromised CI/CD pipelines or malicious build steps.
Detects real-time Multi-Factor Authentication (MFA) OTP relay, commonly associated with Adversary-in-the-Middle (AitM) phishing frameworks like Bluekit. The rule flags successful sign-ins where MFA methods (SMS, TOTP, or Authenticator app) are completed with an abnormally low latency (under 10 seconds), suggesting automated relay, or where discrepancies exist in session context indicative of proxy-based interception.
Detects potential Command and Control (C2) communication associated with Emotet malware by monitoring for successful network connections to suspicious remote ports frequently used by the malware. The rule flags endpoints that establish connections to at least three of these high-risk ports within a one-hour window, which is indicative of automated C2 beaconing behavior.
This rule monitors DNS queries and network connections for access to a set of known domain names associated with Adversary-in-the-Middle (AiTM) phishing infrastructure. It correlates data from DNS events, device network logs, and virtual machine network connections to identify potential interactions with malicious phishing sites typically used to capture user credentials or session tokens.
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