Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,270 detections
Filters
Last updated
All Time
Detection languages
3,342
1,086
505
478
472
Contributors
1,824
1,622
874
505
300
Categories
3,031
2,542
1,076
1,049
929
Platforms
39,960
7,270
6,537
3,868
3,651
Products / Services
1,331
1,109
991
658
494
MITRE Techniques
2,271
1,336
1,318
904
885
CVEs
66
65
33
32
28
IDS Classtypes
81
80
56
27
10
IDS Protocols
149
98
6
5
4
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects the execution of command shells or common post-exploitation utilities as child processes of network appliance management services (e.g., VPN gateways, firewalls, web servers). This pattern indicates potential exploitation of public-facing infrastructure where an attacker achieves code execution through the management daemon or its web components.
Detects a suspicious sequence of reconnaissance activities associated with ShinyHunters campaigns targeting PeopleSoft environments. The pattern involves reading '/etc/hosts' using standard utilities ('cat', 'grep', 'awk') followed by rapid network discovery or probing attempts using 'curl', 'wget', 'nmap', or 'nc' from the same host within a 10-minute window.
Detects the creation of ransom or extortion note files within known web server directories immediately following suspicious HTTP activity targeting an internet-facing host. This pattern aligns with known ShinyHunters extortion tactics where applications are exploited to deploy extortion notes.
Detects the deployment and activity of the MeshCentral remote management agent (meshagent), including process execution, service creation, and network communication to MeshCentral infrastructure. This behavior is indicative of unauthorized persistence and remote command-and-control, often associated with post-compromise activity such as that observed by ShinyHunters.
Detects the execution of the GigaWiper malware or the use of common system commands associated with destructive data activities, such as recursive file removal, disk formatting, or secure data overwriting, often used by threat actors for extortion.
This rule detects when common web server processes or service accounts spawn unexpected interpreter, shell, or reconnaissance tools, which is a common indicator of post-exploitation activity following a successful Remote Code Execution (RCE) attempt.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
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.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
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 anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
Detects HTTP POST requests directed at Grav CMS endpoints where the __unique_form_id__ parameter contains directory traversal sequences (e.g., ../). This behavior is indicative of attempted exploitation of CVE-2026-42608, aimed at bypassing file system restrictions to write files to unauthorized locations outside the application's expected form data directory.
Detects the on-disk presence of a malicious Firefox browser extension, 'pdf-para-texto@extensao.local', used for credential or data harvesting. The rule monitors for the creation of its manifest file or XPI package in specific directories, indicating the staging or installation of the malicious extension.
Page 35 of 347