Trapster Community

Trapster Community is a low-interaction honeypot designed to be deployed on internal networks. It is built to monitor and detect suspicious activities, providing a deceptive layer to network security.

Visit the Trapster website to learn more about our commercial product, which includes advanced features like pre-configured hardened OS, automatic deployment, webhook, SIEM integration and much more...

Features

• Deceptive Security: Mimics network services to lure and detect potential intruders.
• Asynchronous Framework: Utilizes Python's asyncio for efficient, non-blocking operations.
• Configuration Management: Easily configurable through trapster.conf.
• Expandable Services: Add and configure as many services as needed with minimal effort.
• HTTP Honeypot Engine with AI capabilities: Clone any website using YAML configuration, and use AI to generate responses to some HTTP requests.

Supported Protocols

Protocol	Notes
DNS	Works as a proxy to a real DNS server
HTTP/HTTPS	Features custom YAML configuration templating engine
FTP	Capture FTP login attempts
LDAP	Capture LDAP login attempts
MSSQL	Capture MSSQL login attempts
POSTGRES	Capture POSTGRES login attempts
RDP	Capture RDP login attempts
SNMP	Capture SNMP login attempts
SSH	Capture SSH login attempts
TELNET	Capture Telnet login attempts
VNC	Capture VNC login attempts
RSYNC	Capture RSYNC login attempts

Documentation and installation guide

https://docs.trapster.cloud/community/

Logs

Format

Each module can generate up to four types of logs: connection, data, login, and query.

• connection: Indicates that a connection has been made to the module.
• data: Represents raw data that has been sent, logged in HEX format. This data is unprocessed.
• login: Captures login attempts to the module. The data field is in JSON format and contains processed information.
• query: Logs data that has been processed and does not correspond to an authentication attempt. The data field is in JSON format and contains processed information.

HTTP Engine with AI capabilities

The HTTP module can emulate any website. It works with YAML configuration files to match requests using regular expressions, and can generate responses using either a template or an AI model.

The configuration are stored in trapster/data/http, each folder represent a website. An example of the functionnalities can be found at trapster/data/http/demo_api/config.yaml

Structure:

• config.yaml: contains the configuration for the website.
• files/: contains the static files for the website.
• templates/: contains the templates for the website, it supports jinja2 syntax.

AI ALPHA support

To generate responses, you can use the ai field in the configuration. For now, it uses OVHCloud AI Endpoints as it is still free, and in alpha. The file trapster/modules/libs/ai.py contains the code to generate responses using the AI model. It is still very basic, and will be improved in the near future.

For example, this image show a request to capture SQLi attempts, and the response generated by the AI model.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a pull request.

License

Trapster is licensed under the GNU Affero General Public License v3 or later (AGPLv3+). See the LICENSE file for more details.