Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,139 detections
Filters
Last updated
All Time
Detection languages
3,217
1,086
504
478
469
Contributors
1,824
1,607
872
505
300
Categories
2,971
2,471
1,060
1,026
908
Platforms
39,762
7,139
6,517
3,851
3,599
Products / Services
1,324
1,067
970
657
487
MITRE Techniques
2,228
1,317
1,314
873
871
CVEs
66
65
33
32
28
IDS Classtypes
81
80
56
27
10
IDS Protocols
149
98
6
5
4
Detects instances where a LiteLLM proxy is utilized to invoke sensitive Model Context Protocol (MCP) tools (e.g., database, github, slack, jira, ci/cd) while authentication is bypassed ('allow_all_keys=true') and no explicit API key is provided. This indicates a potential unauthorized access attempt to sensitive integrations via a misconfigured proxy.
Detects instances where a LiteLLM proxy is utilized to invoke sensitive Model Context Protocol (MCP) tools (e.g., database, github, slack, jira, ci/cd) while authentication is bypassed ('allow_all_keys=true') and no explicit API key is provided. This indicates a potential unauthorized access attempt to sensitive integrations via a misconfigured proxy.
Detects outbound network connections to 'registry.npmjs.org' originating from 'node' or 'bun' processes. This behavior may indicate an attempt to exfiltrate data, interact with malicious packages, or perform credential harvesting via npm tokens during execution.
Detects Linux processes masquerading as 'php-fpm' or 'postgres' that match known SHA-256 hashes associated with the RedTail malware family. The rule identifies processes attempting to hide their identity using techniques similar to prctl(PR_SET_NAME) to rename themselves while running malicious code.
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
Detects text/report artifacts (markdown, JSON, plain text) produced by an autonomous AI-driven vulnerability research pipeline that decompiles binaries, traces cross-references, hypothesizes memory-safety flaws, and generates/debugs proof-of-concept exploits
Detects anomalous activity patterns consistent with automated firmware reverse engineering pipelines, specifically correlating the high-frequency execution of firmware analysis tools (e.g., binwalk, Ghidra) with the creation of vulnerability research knowledge base artifacts. This behavioral heuristic aims to identify potential AI-driven or automated zero-day discovery workflows by tracking tool usage density and output characteristics.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
Detects cmd.jar, a JAR-based command executor used by threat actors on compromised Cisco Secure FMC to run arbitrary shell commands via /bin/sh -c
Detects the execution of a reverse shell on Linux systems utilizing a common technique involving mkfifo (named pipe) to redirect input/output for a shell session to a netcat network connection. This is often used by attackers to gain interactive remote access to a compromised host.
Detects the execution of a reverse shell on Linux systems utilizing a common technique involving mkfifo (named pipe) to redirect input/output for a shell session to a netcat network connection. This is often used by attackers to gain interactive remote access to a compromised host.
Detects the execution of a reverse shell on Linux systems utilizing a common technique involving mkfifo (named pipe) to redirect input/output for a shell session to a netcat network connection. This is often used by attackers to gain interactive remote access to a compromised host.
Detects instances where a development-oriented web server (such as Vite or Node.js) is configured to bind to all network interfaces ('0.0.0.0') and is simultaneously receiving inbound connections from non-private, external IP addresses on a common development port (5173). This rule filters out common CI/CD environments to focus on potentially insecure exposure of development tools to the public internet.
Detects instances where a development-oriented web server (such as Vite or Node.js) is configured to bind to all network interfaces ('0.0.0.0') and is simultaneously receiving inbound connections from non-private, external IP addresses on a common development port (5173). This rule filters out common CI/CD environments to focus on potentially insecure exposure of development tools to the public internet.
Detects attempts to access sensitive files like environment variables (.env), cloud provider credentials (AWS/Azure), or Terraform state files via a web server interface, specifically targeting Vite development server deployments.
Detects attempts to access sensitive files like environment variables (.env), cloud provider credentials (AWS/Azure), or Terraform state files via a web server interface, specifically targeting Vite development server deployments.
This rule detects inbound HTTP requests targeting a Vite development server that attempt to access the '.env' configuration file via the '//@fs/' path, which is a known technique for sensitive file exposure in misconfigured Vite environments.
This rule detects inbound HTTP requests targeting a Vite development server that attempt to access the '.env' configuration file via the '//@fs/' path, which is a known technique for sensitive file exposure in misconfigured Vite environments.
Detects high-volume bursts of HTTP GET requests against specific paths associated with a Vite development server (/@vite/client, /@fs/, /src/) on port 5173. This pattern is indicative of automated reconnaissance or vulnerability scanning targeting an exposed development environment.
Page 106 of 340