Krawl

A modern, customizable web honeypot server designed to detect and track malicious activity from attackers and web crawlers through deceptive web pages, fake credentials, and canary tokens.

Table of Contents

• Demo
• What is Krawl?
• Krawl Dashboard
• Deployment Modes
• Krawl Banlist
• Quickstart
  • Docker Run
  • Docker Compose
  • Kubernetes
  • Uvicorn (Python)
• Configuration
  • config.yaml
  • Environment Variables
• Ban Malicious IPs
• IP Reputation
• Running Behind a Reverse Proxy or CDN
• Metrics & Monitoring
• Additional Documentation
• Deception using AI
• Contributing

Demo

Tip: crawl the robots.txt paths for additional fun

Krawl URL: http://demo.krawlme.com

View the dashboard http://demo.krawlme.com/das_dashboard

What is Krawl?

Krawl is a cloud‑native deception server designed to detect, delay, and analyze malicious attackers, web crawlers and automated scanners.

It creates realistic fake web applications filled with low‑hanging fruit such as admin panels, configuration files, and exposed fake credentials to attract and identify suspicious activity.

By wasting attacker resources, Krawl helps clearly distinguish malicious behavior from legitimate crawlers.

It features:

• AI Generated Deception Pages: Let attackers help generate your fake vulnerable attack surface
• Spider Trap Pages: Infinite random links to waste crawler resources based on the spidertrap project
• Fake Login Pages: WordPress, phpMyAdmin, admin panels
• Honeypot Paths: Advertised in robots.txt to catch scanners
• Fake Credentials: Realistic-looking usernames, passwords, API keys
• Canary Token Integration: External alert triggering
• Random server headers: Confuse attacks based on server header and version
• Real-time Dashboard: Monitor suspicious activity
• Customizable Wordlists: Easy JSON-based configuration
• Random Error Injection: Mimic real server behavior

You can easily expose Krawl alongside your other services to shield them from web crawlers and malicious users using a reverse proxy. For more details, see the Reverse Proxy documentation.

Krawl Dashboard

Krawl provides a comprehensive dashboard, accessible at a random secret path generated at startup or at a custom path configured via KRAWL_DASHBOARD_SECRET_PATH. This keeps the dashboard hidden from attackers scanning your honeypot.

The dashboard is organized in six tabs:

• Overview: high-level view of attack activity: an interactive map of IP origins, recent suspicious requests, and top IPs, User-Agents, and paths.

• Attacks: detailed breakdown of captured credentials, honeypot triggers, and detected attack types (SQLi, XSS, path traversal, etc.) with charts and tables.

• IP Insight: in-depth forensic view of a selected IP: geolocation, ISP/ASN info, reputation flags, behavioral timeline, attack type distribution, and full access history.

Additionally, after authenticating with the dashboard password, two protected tabs become available:

• Tracked IPs: maintain a watchlist of IP addresses you want to monitor over time.
• IP Banlist: manage IP bans, view detected attackers, and export the banlist in raw or IPTables format.
• Deception: manage AI generated pages, export them or import new ones.

For more details, see the Dashboard documentation.

Deployment Modes

Krawl supports two deployment modes, controlled by the mode setting in config.yaml or the KRAWL_MODE environment variable.

Standalone: ideal for development environments or homelabs with low request counts. Zero additional configuration needed, just run Krawl and it works.

• Single container deployment with no external dependencies
• Lower RAM and resource usage

Scalable: designed for production environments or high-traffic honeypots. The Helm chart defaults to this mode.

• Faster, more responsive dashboard thanks to Redis multi-tier caching
• Lower disk I/O with Redis acting as a hot-path cache in front of PostgreSQL
• Horizontal scaling increase the number of Krawl replicas behind a load balancer

For detailed configuration, Docker Compose examples, Kubernetes/Helm setup, and step-by-step migration instructions, see the Deployment Modes documentation.

Krawl Banlist

Krawl maintains a regularly updated banlist.txt of IP addresses from attackers that triggered its honeypot traps. The banlist is published weekly and available for download, helping the community preemptively block known malicious actors even without using Krawl.

The banlist can also be fetched directly from: https://demo.krawlme.com/das_dashboard/api/export-ips?categories=attacker&fwtype=raw.

Sharing banlists between instances

Krawl instances can federate their banlists: each one can publish its own list on an unauthenticated path and pull in lists from other instances (or any plain-text IP list). Fetched IPs are merged into the local ban decisions and shown in the dashboard.

banlist:
  # Public, unauthenticated download path for this instance's banlist.
  # Supports the same ?categories= and ?fwtype= parameters as the main API.
  # Empty = disabled.
  export_path: "/public_banlist.txt"

  # Upstream banlists to fetch and merge. Plain ".txt" lists work too.
  sources:
    - "https://demo.krawlme.com/das_dashboard/api/export-ips?categories=attacker&fwtype=raw"
    - "https://krawl.example.com/public_banlist.txt"

  refresh_interval: 3600  # seconds between fetches

Quickstart

Docker Run

Run Krawl in standalone mode with the latest image:

docker run -d \
  -p 5000:5000 \
  -e KRAWL_DASHBOARD_SECRET_PATH="/my-secret-dashboard" \
  -e KRAWL_DASHBOARD_PASSWORD="my-secret-password" \
  -v krawl-data:/app/data \
  --name krawl \
  ghcr.io/blessedrebus/krawl:latest

Access the server at http://localhost:5000

Docker Compose

Create a docker-compose.yaml with one of the two deployment modes.

Standalone: just Krawl server with Sqlite storage:

services:
  krawl:
    image: ghcr.io/blessedrebus/krawl:latest
    container_name: krawl-server
    ports:
      - "5000:5000"
    environment:
      - CONFIG_LOCATION=config.yaml
      # - KRAWL_DASHBOARD_PASSWORD=my-secret-password
    volumes:
      - ./config.yaml:/app/config.yaml:ro
      - krawl-data:/app/data
    restart: unless-stopped

volumes:
  krawl-data:

Scalable: with PostgreSQL and Redis:

[!CAUTION] The example below uses default passwords (krawl/krawl). Change them before deploying to production.

services:
  postgres:
    image: postgres:16-alpine
    environment:
      POSTGRES_DB: krawl
      POSTGRES_USER: krawl
      POSTGRES_PASSWORD: krawl
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U krawl -d krawl"]
      interval: 10s
      timeout: 5s
      retries: 5

  redis:
    image: redis:7-alpine
    volumes:
      - redis_data:/data
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5

  krawl:
    image: ghcr.io/blessedrebus/krawl:latest
    container_name: krawl-server
    ports:
      - "5000:5000"
    environment:
      - CONFIG_LOCATION=config.yaml
      - KRAWL_MODE=scalable
      - KRAWL_POSTGRES_HOST=postgres
      - KRAWL_POSTGRES_PORT=5432
      - KRAWL_POSTGRES_USER=krawl
      - KRAWL_POSTGRES_PASSWORD=krawl
      - KRAWL_POSTGRES_DATABASE=krawl
      - KRAWL_REDIS_HOST=redis
      - KRAWL_REDIS_PORT=6379
      # - KRAWL_DASHBOARD_PASSWORD=my-secret-password
    volumes:
      - ./config.yaml:/app/config.yaml:ro
    restart: unless-stopped
    depends_on:
      postgres:
        condition: service_healthy
      redis:
        condition: service_healthy

volumes:
  postgres_data:
  redis_data:

To deploy, just run

docker compose up -d

Production-ready compose files are also available in the docker/ directory. For development (builds from source with hot-reload), use the compose files in docker/dev/.

For more details on both modes, see Deployment Modes.

Kubernetes

Krawl is also available natively on Kubernetes. Installation can be done either via manifest or using the Helm chart.

The Helm chart defaults to scalable mode with bundled PostgreSQL and Redis:

helm install krawl oci://ghcr.io/blessedrebus/krawl-chart --version 2.3.0 \
  -n krawl-system --create-namespace \
  --set postgres.password=your-password \
  --set redis.password=your-redis-password \
  --set dashboardPassword=your-dashboard-password \
  --set config.dashboard.secret_path=/my-secret-dashboard

Minimal example values files are provided for both modes:

• values-minimal.yaml ---> Scalable (default)
• values-standalone.yaml ---> Standalone

See Deployment Modes and Chart documentation for full configuration and migration instructions.

Uvicorn (Python)

Run Krawl directly with Python 3.13+ and uvicorn for local development or testing:

pip install -r requirements.txt
uvicorn app:app --host 0.0.0.0 --port 5000 --app-dir src --no-server-header

Access the server at http://localhost:5000

Configuration

Krawl uses a configuration hierarchy in which environment variables take precedence over the configuration file. This approach is recommended for Docker deployments and quick out-of-the-box customization.

Configuration via config.yaml

You can use the config.yaml file for advanced configurations, such as Docker Compose or Helm chart deployments.

Configuration via Environmental Variables

All settings can be supplied as environment variables, which override config.yaml. The variable name is KRAWL_ plus the setting path in upper case, so dashboard.password becomes KRAWL_DASHBOARD_PASSWORD.

For example

# Set canary token
export CONFIG_LOCATION="config.yaml"
export KRAWL_CANARY_TOKEN_URL="http://your-canary-token-url"

# Set number of pages range (min,max format)
export KRAWL_LINKS_PER_PAGE_RANGE="5,25"

# Set analyzer thresholds
export KRAWL_HTTP_RISKY_METHODS_THRESHOLD="0.2"
export KRAWL_VIOLATED_ROBOTS_THRESHOLD="0.15"

# Set custom dashboard path and password
export KRAWL_DASHBOARD_SECRET_PATH="/my-secret-dashboard"
export KRAWL_DASHBOARD_PASSWORD="my-secret-password"

Example of a Docker run with env variables (standalone mode):

docker run -d \
  -p 5000:5000 \
  -e KRAWL_MODE=standalone \
  -e KRAWL_PORT=5000 \
  -e KRAWL_DELAY=100 \
  -e KRAWL_DASHBOARD_PASSWORD="my-secret-password" \
  -e KRAWL_CUSTOM_TEMPLATE_PATH="/templates/custom_page.html" \
  -e KRAWL_CANARY_TOKEN_URL="http://your-canary-token-url" \
  --name krawl \
  ghcr.io/blessedrebus/krawl:latest

Use Krawl to Ban Malicious IPs

Krawl uses a reputation-based system to classify attacker IP addresses and provides two ways to export IP lists for firewall integration.

The /api/export-ips endpoint queries the database directly and supports filtering by IP category (attacker, bad_crawler, regular_user, good_crawler) and output format (raw, iptables, nftables):

curl "https://your-krawl-instance/<DASHBOARD-PATH>/api/export-ips?categories=attacker&fwtype=raw"

This enables automatic blocking of malicious traffic across various platforms:

• OPNsense and pfSense
• RouterOS
• IPtables and Nftables
• Fail2Ban

For full API parameters, examples, and adding custom firewall formats, see the Firewall Exporters documentation.

Krawl can also push banned IPs directly to a Cloudflare Account IP List for use in WAF rules. The sync runs as a background task and updates the list by full replacement on a configurable interval. See the Cloudflare Banlist Sync documentation.

IP Reputation

Krawl uses tasks that analyze recent traffic to build and continuously update an IP reputation score. It runs periodically and evaluates each active IP address based on multiple behavioral indicators to classify it as an attacker, crawler, or regular user. Thresholds are fully customizable.

The analysis includes:

• Risky HTTP methods usage (e.g. POST, PUT, DELETE ratios)
• Robots.txt violations
• Request timing anomalies (bursty or irregular patterns)
• User-Agent consistency
• Attack URL detection (e.g. SQL injection, XSS patterns)

Each signal contributes to a weighted scoring model that assigns a reputation category:

• attacker
• bad_crawler
• good_crawler
• regular_user
• unknown (for insufficient data)

The resulting scores and metrics are stored in the database and used by Krawl to drive dashboards, reputation tracking, and automated mitigation actions such as IP banning or firewall integration.

AI-Generated Deception Pages

Krawl can automatically generate realistic deception pages using AI models from OpenRouter or OpenAI APIs. This feature creates unique, plausible honeypot pages on-the-fly to deceive attackers without manual page creation.

Key Features:

• Dynamic Generation: Creates unique HTML pages for any request path
• Smart Caching: Caches generated pages to avoid redundant API calls
• Daily Rate Limiting: Control API costs with configurable request limits
• Multiple Providers: Support for OpenRouter (free options) and OpenAI
• Graceful Fallback: Falls back to standard honeypot when disabled or limit reached
• Cached Serving: Previously generated pages served even when AI is disabled

Quick Setup:

ai:
  enabled: true
  provider: "openrouter"
  openai_base_url: "your-custom-base-url"
  api_key: "your-api-key"
  model: "nvidia/nemotron-3-super-120b-a12b:free"
  timeout: 60
  max_daily_requests: 10

For detailed configuration and usage, see the AI Generation documentation.

You can also contribute deception templates by opening a PR, see Contributing Deception Templates.

Running Behind a Reverse Proxy or CDN

NGINX, Traefik, and other proxies like CloudFlare need header forwarding so Krawl can see the real IP. See the Reverse Proxy documentation for configuration examples and the full header list.

Metrics & Monitoring

Krawl exposes Prometheus metrics at /<dashboard_secret_path>/metrics (enabled by default) and ships with a ready-to-import Grafana dashboard at grafana-dashboard.json.

See the Monitoring documentation for the full metric list, Grafana import steps, and Prometheus / Kubernetes (ServiceMonitor) scraping setup.

Additional Documentation

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request (explain the changes!)

Disclaimer

[!CAUTION] This is a deception/honeypot system. Deploy in isolated environments and monitor carefully for security events. Use responsibly and in compliance with applicable laws and regulations.

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