Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects outbound network requests from non-standard Android application processes to Google's generative-AI API endpoints (e.g., Gemini). This behavior is indicative of malware, such as RatHat, attempting to exfiltrate accessibility information or UI trees to an LLM to facilitate automated screen interaction and navigation.
Detects outbound network requests from non-standard Android application processes to Google's generative-AI API endpoints (e.g., Gemini). This behavior is indicative of malware, such as RatHat, attempting to exfiltrate accessibility information or UI trees to an LLM to facilitate automated screen interaction and navigation.
Detects the loading of spoofed system DLLs (dnsapi.dll or ws2_32.dll) from non-system directories, followed by network beaconing to suspicious domains associated with the NeedyMantis malware.
Detects network activity associated with the NeedyMantis malware command and control (C2), including DNS queries for the domain corp.tripswithengine.com, TLS SNI identification, and specific HTTP beaconing patterns to /library/zip/ with a hardcoded user agent.
Detects network patterns associated with NeedyMantis-style command-and-control (C2) activity. This includes specific HTTP GET requests with hardcoded User-Agent strings, cookie-based binary data transfer in server responses, and WebSocket communication attempting to establish binary protocol channels.
Detects various indicators of compromise (IOCs) including known malicious file hashes, suspicious file names commonly associated with staging or initialization in web directories, and network communication to known malicious domains or URLs. It also monitors process execution command lines for references to these IOCs, excluding common browser processes.
This rule monitors for indicators of compromise (IOCs) associated with known malware and C2 infrastructure, including specific IP:port combinations, malicious domains, URLs for file downloads, and SHA256 hashes of known malware. The rule correlates data across device network events, file events, and process execution events to identify potential infections or communication with malicious infrastructure.
This rule detects outgoing HTTP POST requests to the '/api/credentials' endpoint, which is characteristic of the AMOS (Atomic macOS Stealer) malware family communicating with its command-and-control server to exfiltrate stolen sensitive information.
Detects HTTP beaconing patterns and TLS SNI traffic associated with the Sauron Loader malware. The rules identify specific URI structures (including versioned resources and various configuration/profile endpoints), specific URL parameters used for session management, and characteristic User-Agent strings. Additionally, it monitors for connections to known command-and-control infrastructure via TLS SNI filtering.
Detects anomalous behavior where a node.exe process establishes an outbound network connection, characteristic of a C2 registration or ping loop, followed shortly by the spawning of an interactive command shell (cmd.exe or powershell.exe) by the same node.exe process. This pattern is indicative of a RAT (Remote Access Trojan) shell handler execution.
Detects two distinct suspicious patterns: first, the deletion of executable files residing in common user-writable temporary directories (e.g., Temp, Downloads, AppData) using command-line tools like 'del' or 'erase', which is often indicative of anti-forensic activity after malware execution. Second, it monitors for network traffic involving HTTP POST requests or specific SOAP headers related to 'tempuri.org', which are commonly associated with default .NET WCF service scaffolding and may be used by adversaries for C2 communication or exfiltration.
Detects two distinct suspicious patterns: first, the deletion of executable files residing in common user-writable temporary directories (e.g., Temp, Downloads, AppData) using command-line tools like 'del' or 'erase', which is often indicative of anti-forensic activity after malware execution. Second, it monitors for network traffic involving HTTP POST requests or specific SOAP headers related to 'tempuri.org', which are commonly associated with default .NET WCF service scaffolding and may be used by adversaries for C2 communication or exfiltration.
Detects the first-ever observed network connection from a device to WhatsApp Web or Telegram Web within a 30-day lookback period. This behavior is used to identify potentially unauthorized companion-device linking to a user's messenger account, which could indicate credential theft or unauthorized access.
This rule monitors for network connections to known malicious domains and IP addresses, as well as the presence or execution of files with specific SHA256 hashes known to be associated with threat activity. The indicators focus on Vercel-hosted domains and specific file hashes linked to recent campaign activity.
Detects NeedyMantis' initial C2 beacon: an HTTP GET to /library/zip/ on port 443, anchored to the URI root, carrying a client-originated 'Set-Cookie:' header (clients normally send 'Cookie:', never 'Set-Cookie:' -- this direction reversal is the core anomaly and is near-impossible for legitimate traffic) alongside an explicit 'Upgrade: websocket' header. Matching the literal header text (not just header-name presence) and anchoring the URI removes generic WebSocket apps and any unrelated path containing '/library/zip/' as a substring.
Detects Kothamine Agent via hardcoded base64 AES-GCM key combined with C2 loop markers and plugin command strings, gated on PE structure to reduce false positives
Detects large outbound HTTP POST requests to known public AI-inference APIs and paste services. This pattern is potentially indicative of data exfiltration or sensitive information exposure where attackers leverage third-party services to bypass traditional outbound egress filtering.
Detects outbound HTTP requests traversing a proxy where the User-Agent string contains structurally consistent but algorithmically randomized components. This pattern is characteristic of AI-generated C2 client code attempting to create plausible yet non-standard browser signatures for each beacon. Detection relies on identifying User-Agent strings that follow a standard browser template (e.g., Chrome/Safari) but include anomalous hex/alphanumeric tokens in the version fields.
Detects unauthorized processes attempting to access the cloud instance metadata service (169.254.169.254). Adversaries frequently target this endpoint to extract sensitive instance information, such as IAM credentials or configuration data, typically as part of post-exploitation discovery or SSRF-based attacks.
Detects suspicious outbound HTTPS POST traffic to major generative AI API endpoints (OpenAI, Anthropic, Google) originating from scripting hosts like powershell.exe or python.exe. The detection is triggered by either unusually large outbound payload sizes or the use of unauthorized scripting tools, which is indicative of data exfiltration using AI infrastructure as a relay.
Detects suspicious processes such as PowerShell, Python, or scripting hosts performing network communication with major AI/LLM provider APIs, followed immediately by child process execution or related activity. This pattern is indicative of LLM-assisted automation for malicious activities, potentially using AI to generate code, scripts, or orchestrate command and control actions.
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