Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
811 detections
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Detects HTTP traffic associated with the NeedyMantis malware, specifically identifying initial beacons to a known C2 domain using a specific User-Agent, followed by command-and-control response headers that suggest the exfiltration of reconnaissance data via the Set-Cookie field.
Detects execution of various reconnaissance commands commonly used by Remote Access Trojans (RATs) to profile a target host. This includes querying security product status (AV/Defender), enumerating domain controllers, network adapters, port scanning, and gathering system hardware and software inventory.
Detects automated reconnaissance using WMI or PowerShell to perform broad queries for system environment, security products, network adapters, software, and hardware attributes. The detection focuses on clusters of distinct WMI reconnaissance categories occurring within a 15-minute window, which is often indicative of pre-C2 profiling by adversaries.
Detects a suspicious sequence of command execution patterns characteristic of the Kothamine agent performing post-compromise system discovery. The rule triggers when multiple discovery commands (tasklist, ipconfig, taskkill) are executed by the same process instance within a 5-minute window, consistent with typical C2 behavior for reconnaissance.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous discovery behavior originating from known AI agent and development tools (e.g., Claude, Cursor, Aider). The rule monitors for a rapid sequence of commands spanning multiple categories of enumeration (Network, File/Directory, Software, and System) within a short timeframe, which indicates an attempt to map the runtime environment, permissions, or system capabilities.
Detects host-based reconnaissance activity involving system, network, and security software enumeration using standard administrative tools (e.g., PowerShell, WMI, netsh) that is followed by an outbound network connection to a known or suspicious implant check-in endpoint within a 30-minute window.
Detects automated reconnaissance using WMI or PowerShell to perform broad queries for system environment, security products, network adapters, software, and hardware attributes. The detection focuses on clusters of distinct WMI reconnaissance categories occurring within a 15-minute window, which is often indicative of pre-C2 profiling by adversaries.
Detects automated reconnaissance using WMI or PowerShell to perform broad queries for system environment, security products, network adapters, software, and hardware attributes. The detection focuses on clusters of distinct WMI reconnaissance categories occurring within a 15-minute window, which is often indicative of pre-C2 profiling by adversaries.
Detects the execution of the sckit Go implant, linked to the MemTensor supply chain compromise, during its host-profiling stage. The rule monitors for the 'sckit' binary being executed with 'stage0' and '--config64' command-line arguments, or being spawned by legitimate runtime interpreters like Node.js or Python, indicating an attempt to profile host information (timezone, language, privileges) and subsequent self-deletion.
Detects the ChainScript RAT, executing via a masqueraded Node.js process, enumerating Local Extension Settings for browser wallet plugins. This reconnaissance activity is a precursor to exfiltrating crypto wallet data.
Detects reconnaissance and situational-awareness collection commands (e.g., tasklist, systeminfo, wmic, powershell) executed by processes matching suspected persistence artifacts associated with known malicious activity (specifically referencing potential Telegram C2 tasking). The rule monitors common discovery utilities spawned by suspicious process names or paths.
Detects anomalous behavioral patterns where an AI agent process (e.g., Claude Code, Cursor, Aider) performs multi-category discovery of its own runtime environment, plugins, and host configuration. The rule identifies agents that trigger processes spanning at least three distinct discovery categories (Network, File/Plugin, Software, or System) within a short timeframe, indicating potential reconnaissance of agent-authorized capabilities and tools rather than standard host exploration. Covers T1082, T1518, T1083, T1016
Detects malicious network activity associated with TokenGrabber stealer malware, specifically identifying outbound HTTP requests to public IP lookup services (api.ipify.org) followed by chained geolocation lookups (ip-api.com). This behavior is characteristic of an initial reconnaissance phase where the malware determines the victim's external network footprint.
Detects malicious network activity associated with TokenGrabber stealer malware, specifically identifying outbound HTTP requests to public IP lookup services (api.ipify.org) followed by chained geolocation lookups (ip-api.com). This behavior is characteristic of an initial reconnaissance phase where the malware determines the victim's external network footprint.
Detects anomalous behavioral patterns where an AI agent process (e.g., Claude Code, Cursor, Aider) performs multi-category discovery of its own runtime environment, plugins, and host configuration. The rule identifies agents that trigger processes spanning at least three distinct discovery categories (Network, File/Plugin, Software, or System) within a short timeframe, indicating potential reconnaissance of agent-authorized capabilities and tools rather than standard host exploration. Covers T1082, T1518, T1083, T1016
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects processes that access multiple categories of sensitive data (browser autofill, cryptocurrency wallets, password manager exports, or system fingerprinting files) within a short time window. This behavior is highly indicative of modular infostealers or data-collection malware conducting 'stacking access' to stage sensitive information for exfiltration.
Detects the execution of multiple distinct host reconnaissance commands (e.g., systeminfo, whoami, ipconfig, AV/EDR checks, and domain enumeration) within a short window (10 minutes) originating from the same parent process. This pattern is characteristic of automated reconnaissance collection, often performed during the initial phases of an attack or as part of a C2 workflow. The rule includes exclusions for common administrative, monitoring, and RMM tools to reduce false positives.
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