Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
20,483 detections
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IDS Classtypes
1
Detects the execution of NetSupport Remote Access Tool (RAT) components (client32.exe, Nskbfltr.sys, or TCCTL32.dll) from locations outside of the standard program installation directories. This behavior is often indicative of unauthorized persistence or masquerading by malware utilizing remote support software for C2.
Detects the execution of programs directly from the root of a removable or virtual drive (e.g., ISO, IMG, VHD) via explorer.exe. This activity is a hallmark of phishing attacks that leverage container mounting to bypass Mark-of-the-Web (MOTW) security protections, as files extracted from or executed within mounted containers often fail to inherit MOTW metadata.
Detects the use of forfiles.exe to execute command-line interpreters such as cmd.exe or powershell.exe. This pattern is commonly used by adversaries as a LOLBAS (Living Off the Land Binary and Script) technique to achieve indirect command execution and bypass security controls.
Detects the use of pcalua.exe to launch executables or scripts located in potentially transient or user-writable directories such as Temp, Downloads, or AppData. This behavior is indicative of an adversary attempting to execute payloads while bypassing security controls or masquerading as legitimate system processes.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
This rule detects potentially suspicious activity involving conhost.exe, a process typically associated with console applications. It identifies conhost.exe instances running from suspicious paths (e.g., Temp, Downloads, AppData) or launched by unexpected parent processes. Additionally, it detects scenarios where conhost.exe acts as the parent process to common command-line or scripting utilities, which is an atypical behavior often indicative of an attempt to obscure malicious command execution.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
Detects endpoint behavior consistent with AI-assisted exploit development, characterized by the execution of reverse engineering and vulnerability research tools (e.g., IDA Pro, Ghidra, AFL) interleaved with frequent outbound network connections to LLM APIs, followed by the local compilation of a new executable or DLL.
This rule detects a malicious execution sequence often associated with VBScript droppers. It identifies the execution of a process (specifically notepad++.exe) from a dynamically named staging directory under C:\Users\Public\, followed within 10 minutes by the deletion of the associated staging archive (Evernote.zip).
This rule detects interactive or user-triggered execution of PowerShell carrying indicators of payload downloading, inline evaluation, or Base64 encoding. Because explorer.exe is the parent process, it typically signifies a payload delivered via email/web that a user opened directly such as an .lnk shortcut.
This rule monitors for two distinct suspicious patterns: first, the interaction between Claude Desktop and the CEF (Chromium Embedded Framework) library, which may indicate unauthorized plugin or extension loading; second, the execution of non-standard binaries initiated by various JetBrains IDE processes located outside of standard program directories, which could indicate process hollowing or unauthorized tool execution.
Detects rapid, multi-category reconnaissance activity initiated by AI-agent binaries (e.g., Claude, Cursor, ChatGPT). The rule identifies sessions that perform four or more distinct discovery operations—spanning account, network service, system information, and network configuration discovery—within a short time window, indicating potentially malicious autonomous exploration by an AI agent. Covers T1046, T1087, T1083, T1016
Detects potential multimodal prompt injection attacks where an AI agent process ingests a file (PDF, image, etc.) from a browser or mail client and subsequently performs suspicious downstream activity, such as spawning a shell with execution primitives or making network connections to rare, non-reputable external domains. Covers T1204, T1059, T1105
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
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 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
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