Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects unauthorized process activity that reads, copies, archives, or transmits the NetScaler ns.conf configuration file. This file contains highly sensitive information, including AAA/LDAP bind credentials and certificates. Adversaries may stage or exfiltrate this file following the exploitation of vulnerabilities (such as CVE-2026-88771 or CVE-2026-88772) on NetScaler appliances to facilitate further network compromise.
This analytic detects the use of `curl.exe` with insecure flags such as `-k`, `--insecure`, `--proxy-insecure`, or `--doh-insecure`
which disable TLS certificate validation.
It leverages Cisco Network Visibility Module (NVM) flow data and process arguments
to identify outbound connections initiated by curl where TLS checks were explicitly disabled.
This behavior may indicate an attempt to bypass certificate validation to connect to potentially untrusted or malicious endpoints,
a common tactic in red team operations, malware staging, or data exfiltration over HTTPS.
which disable TLS certificate validation.
It leverages Cisco Network Visibility Module (NVM) flow data and process arguments
to identify outbound connections initiated by curl where TLS checks were explicitly disabled.
This behavior may indicate an attempt to bypass certificate validation to connect to potentially untrusted or malicious endpoints,
a common tactic in red team operations, malware staging, or data exfiltration over HTTPS.
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.
Detects an anomalous volume of file downloads from SharePoint or OneDrive by a user on an unmanaged, non-compliant device, shortly following a suspicious or risky sign-in event. The rule establishes a baseline for individual user behavior over 30 days and alerts when the current hourly download activity significantly exceeds that baseline.
This rule detects potentially compromised accounts by correlating suspicious authentication activity (such as risky sign-ins, impossible travel, or malicious OAuth/Mail-rule configurations) with a subsequent spike in SharePoint or OneDrive file access, downloads, or external link sharing within a one-hour window. This behavior is indicative of an adversary performing cloud-based data exfiltration after gaining unauthorized access to an account.
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.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects the installation of unauthorized AI-related browser extensions followed by outbound network communication from the browser process to known external AI service API endpoints within one hour. This behavior is indicative of potential data exfiltration via shadow AI tools, bypassing standard enterprise DLP controls.
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.
This rule detects anomalous activity associated with the Antino backdoor, which uses Microsoft OneDrive as a dead-drop command-and-control (C2) channel. It identifies specific file paths related to heartbeat check-ins, operator tool drops, and exfiltration retrieval, summarizing events by user and IP to highlight potential malicious persistent communication and data transfer patterns.
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.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects the installation of unauthorized AI-related browser extensions followed by outbound network communication from the browser process to known external AI service API endpoints within one hour. This behavior is indicative of potential data exfiltration via shadow AI tools, bypassing standard enterprise DLP controls.
Detects the execution of known cloud synchronization utilities like rclone, megasync, or megacmd, which are frequently leveraged by ransomware operators to exfiltrate data to public cloud storage providers. The rule monitors both local process execution with typical command-line arguments and subsequent outbound network connections to common cloud storage domains.
Detects outbound network connections to known SectopRAT C2 infrastructure, specifically targeting the hardcoded IP 98.142.252.140 or the non-standard port 15847 often used for encrypted exfiltration.
Detects outbound network connections to common TOR entry, guard, or client service ports, or the execution of TOR-related processes. This behavior may indicate an adversary establishing C2 communication channels or attempting to perform anonymized data exfiltration.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
This rule detects anomalous activity involving cloud-based shell services (AWS CloudShell, Azure Cloud Shell, GCP Cloud Shell). It triggers on two main conditions: (1) First-time usage of these shell environments by a principal within the detection window, and (2) The execution of commands associated with data staging, ingress tool transfer, or exfiltration (e.g., curl, wget, storage copy commands).
Detects anomalous high-volume download or 'GetObject' operations from a single identity across multiple cloud storage objects and buckets within a short time window, which is indicative of bulk data exfiltration.
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