Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where a process writes to known persistence locations, including Windows Registry Run keys, the Windows Startup folder, Linux shell profile configuration files (.bashrc/.bash_profile), and system-level task directories (cron and systemd units). This activity is often indicative of malicious persistence mechanisms, such as those used by compromised AI agents or backdoors to survive system reboots.
This rule detects potential command injection attacks targeting the Zimbra 'swatchdog' service, specifically involving the abuse of SNMP trap notifications (CVE-2026-73570). The attack works by injecting malicious shell metacharacters into the 'zimbra-MIB::zmServiceName' argument, which is processed by a Perl-invoked 'swatchdog' script. The rule monitors process execution events for shell commands spawned by the swatchdog utility that contain suspicious shell operators and the targeted MIB parameter.
Detects potential misuse of SSH credentials by identifying successful SSH logins from source IP addresses that have not been observed in the previous 14 days, or successful SSH logins occurring within one hour of an authorized_keys file modification on the same host. This combination of behaviors is indicative of an adversary establishing or using persistence via SSH key manipulation.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects network connections or DNS resolutions to known anonymous file-sharing, cloud storage, and messaging-bot API endpoints (e.g., transfer.sh, Telegram Bot API, Discord CDN) commonly abused by adversaries for data staging and exfiltration. This rule specifically targets non-browser processes that attempt to communicate with these endpoints, helping identify potentially malicious exfiltration activities.
This rule detects a node.exe process initiating a network connection to a specific remote IP (69.48.229.140) on port 8080, followed by the termination of that same process within 45 seconds of the connection. This behavior is indicative of a short-lived beaconing process or an ephemeral network task associated with command-and-control activity.
Detects anomalous process execution and loopback network activity associated with the Model Context Protocol (MCP) 'exec_in_session' method. This pattern indicates an adversary potentially abusing the AI coding assistant's MCP server capabilities to execute arbitrary commands as a C2 channel.
Detects anomalous process execution and loopback network activity associated with the Model Context Protocol (MCP) 'exec_in_session' method. This pattern indicates an adversary potentially abusing the AI coding assistant's MCP server capabilities to execute arbitrary commands as a C2 channel.
Detects the anomalous behavior of a Node.js process acting as a typosquatting agent. The detection identifies a node.exe process initiating a network connection to a known malicious C2 IP and port, followed by the termination of that same process within 45 seconds. This pattern is consistent with the agent crashing due to an unhandled exception (e.g., os.userInfo() failure) after beaconing.
Detects anomalous process execution by PeopleSoft application server processes (e.g., psappsrv, jsvc) immediately following an inbound network request. This behavior is indicative of exploitation of a Java deserialization gadget chain, such as those used by the threat actor UNC6240 to achieve Remote Code Execution (RCE) and spawn command interpreters.
Detects anomalous authentication patterns where a single source IP targets a high number of unique user accounts with failed authentication attempts over a rolling window. By filtering out SSO/Federated methods and specific service account patterns, this rule isolates low-and-slow brute-force or password-spraying activities designed to evade standard account lockout mechanisms.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
Detects the spawning of shells, network utilities, or scripting interpreters from Citrix NetScaler processes (nsppe or httpd), which may indicate post-exploitation activity following an initial access vulnerability.
Detects command-line activity on a NetScaler appliance indicative of defense evasion by truncating, deleting, or disabling audit logs (ns.log) and syslog forwarding services. This activity often follows the exploitation of public-facing application vulnerabilities where an attacker seeks to hide their presence.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
Detects network connection attempts by known AI agent or development-related runtime processes (python, node, etc.) to external endpoints that are not contained within a defined allowlist of LLM and infrastructure providers. This is intended to identify potential data exfiltration or unauthorized command and control communication originating from LLM-powered applications.
This rule detects known malicious activity including network connections to a specific C2 IP address (69.48.229.140), downloading files from specific C2 URLs, the presence of known malicious file hashes (SHA256), and communications involving specific actor-associated email addresses.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects a potential web shell deployment by identifying a web server process creating or modifying a file in common web directory locations, followed shortly (within 10 minutes) by the execution of that same file.
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