Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potentially malicious behavior associated with web browser processes, including the execution of Python scripts, the loading of browser extensions from non-standard directories like AppData or Temp, the creation of Python scripts by browsers in temp folders, and outbound network connections by Python processes following browser activity. These patterns are often associated with browser-based exploitation, extension-based malware, or initial access stages where a browser is used as a conduit for malicious code execution.
Detects network and DNS activity involving domains known to be used by the threat actor APT28 for command and control, specifically for hosting WebDAV payloads, malicious LNK files, and weaponized documents.
This rule detects the suspicious initialization of the Common Language Runtime (CLR) components (mscoree.dll, clr.dll, or oleaut32.dll) within the explorer.exe process, followed closely by an outbound HTTPS connection to filen.io domains. This pattern is indicative of fileless .NET-based C2 implants, such as those generated by the Covenant framework, executing within the context of a legitimate Windows shell process.
This rule detects the suspicious initialization of the Common Language Runtime (CLR) components (mscoree.dll, clr.dll, or oleaut32.dll) within the explorer.exe process, followed closely by an outbound HTTPS connection to filen.io domains. This pattern is indicative of fileless .NET-based C2 implants, such as those generated by the Covenant framework, executing within the context of a legitimate Windows shell process.
This rule detects a specific typosquatting pattern used by the EvilTokens phishing kit. It looks for HTTP traffic where the host contains 'workers.dev' and matches a regular expression for 'viewdoc[a-z0-9\-]+\.workers\.dev'. This indicates an attempt to mimic legitimate Cloudflare Workers domains for phishing purposes, specifically targeting the 'viewdoc' pattern often seen in document-sharing lures.
This rule detects network traffic indicative of the 'EvilTokens' phishing kit, specifically targeting Microsoft OAuth Device Code flow. It looks for HTTP POST requests to the '/api/device/start' URI on domains hosted on Cloudflare Workers ('.workers.dev'). This pattern suggests an attempt to initiate a device code phishing attack, where adversaries trick users into entering credentials or codes to grant access to malicious applications.
This rule detects network communication (connections, DNS queries, or proxy logs) between endpoints and a list of known malicious domains and IP addresses associated with Mistic and Woodgnat/KongTuke threat families. It identifies these activities across network event logs, DNS queries, and security logs.
Detects potential browser fingerprinting and network reconnaissance behavior associated with Bluekit BitM PhaaS campaigns. The rule correlates large suspicious JavaScript file downloads (excluding common CDNs) with STUN server connectivity within a 10-minute window, a pattern often used by adversary-in-the-middle (AiTM) frameworks to fingerprint victims and establish WebRTC channels.
This rule detects the use of the JSON-RPC 'eth_call' method by non-browser processes during network connection events. It specifically looks for network connections where the initiating process is not a common web browser (Chrome, Edge, Firefox) and the network traffic or command line contains the 'eth_call' method. This behavior is indicative of malware or other malicious software attempting to interact with blockchain services, potentially for dynamic command and control or address resolution, as described in the Check Point research.
Detects browser connections to the Bing Search by Image endpoint, which can be exploited as a Server-Side Request Forgery (SSRF) proxy to bypass Content Security Policies (CSP) and exfiltrate data, as seen in the SearchLeak attack. The rule specifically looks for connections to 'bing.com/images/searchbyimage' that also include the 'imgurl=' or 'imgurl%3d' parameter, indicating an attempt to use Bing's server-side image fetching capability.
This rule detects network connections to known IP addresses associated with the FortiBleed vulnerability exploitation. These IP addresses have been categorized based on their observed roles in FortiBleed attacks, including C2, pentest lab, credential validation, and sniffer nodes. Monitoring these connections can help identify compromised systems communicating with FortiBleed infrastructure.
Detects outbound network connections to major Large Language Model (LLM) API endpoints (OpenAI, Google, Anthropic) initiated by processes not explicitly identified as standard web browsers or command-line utilities. This pattern may indicate automated data exfiltration ('prompt stealing' or unauthorized access to corporate data) using custom or malicious tools executing from suspicious directories like temp, appdata, or public folders.
This rule detects network connections to known IP addresses associated with the FortiBleed vulnerability exploitation. These IP addresses have been categorized based on their observed roles in FortiBleed attacks, including C2, pentest lab, credential validation, and sniffer nodes.
This rule detects a suspicious sequence of network connections indicative of information stealer activity, specifically targeting behaviors observed with malware like VVS Stealer. It identifies instances where a non-browser process, originating from common user-writable or temporary directories (AppData, Temp, Downloads, Users\Public), first connects to an external IP discovery service (ipify.org) and then subsequently communicates with Discord services (discord.com or discordapp.com) within a ten-minute window. This pattern suggests that malware is collecting the victim's public IP address before exfiltrating stolen data (e.g., account or browser information) to Discord infrastructure, often used as a command and control or data exfiltration channel by stealers.
Detects DNS lookups and network connections to known AI API endpoints (OpenAI, Anthropic, Google Generative Language) initiated by non-standard processes. This rule excludes common web browsers, productivity applications, and development tools to identify potentially unauthorized or suspicious interaction with AI services from internal processes.
Detects potential execution chains related to QR code phishing (Quishing) by monitoring for suspicious process creation or outbound network connections originating from document readers, office applications, and QR scanner utilities. It covers three primary vectors: suspicious process spawning (e.g., Office apps launching cmd or powershell), non-standard network connections from document readers, and malicious URL-based LNK files spawned via explorer.exe.
This rule detects the use of JSON-RPC 'eth_call' by a non-browser process during network connection events. It specifically looks for network activity where the process is not a known web browser (Chrome, Edge, Firefox) and the network traffic or command line contains the string '"method":"eth_call"'. This behavior is often associated with malware like SessionGate, Remus, or AnimateClipper attempting dynamic command or address resolution related to blockchain interactions.
Detects network communication from processes to known ClickFix autoupdate domains such as 'autoupdatet.com', 'autoupdaters.com', and 'autoupdatethis.com'. This activity could indicate the presence of unwanted or malicious software attempting to update itself.
Detects a single host making Zulip REST API POST requests to multiple distinct topics, specifically looking for patterns associated with system profiling and command/tasking as observed in ZiChatBot. The rule identifies activity where a source IP uses the Zulip API to send messages to at least two different topics, with the raw request content indicating either 'channel/stream' and 'topic/subject' or keywords like 'system info', 'shellcode', 'task', or 'command'.
Detects anomalous process execution patterns where a database process (postgres or postmaster) or a common system utility (nc, python, bash) is observed initiating unauthorized network connections to a known external C2 IP or accessing sensitive cryptographic files (e.g., server.key). This behavior is indicative of potential post-exploitation activity, specifically the exfiltration of credentials or sensitive data via database-level command execution (sys_eval) or direct file manipulation.
Detects suspected NarwhalRAT activity where a process (e.g., userscreen.exe or pythonw.exe in suspicious locations) communicates with the pCloud API to resolve a dead-drop location, followed within 60 seconds by a beacon connection to a different remote IP, characteristic of C2 communication.
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