Monitors test coverage for the Beelzebub project, with integration visible through badges and references to coverage reports
Provides containerized deployment for Beelzebub with ready-to-use Docker Compose configurations
Offers official ELK stack integration for log management and analysis through documented integration paths
Supported documentation platform shown in the project's sponsors section
Runs automated CI pipelines for testing, code quality checks, and Docker image building
Enables Kubernetes deployment through Helm charts with support for installation and upgrades
Official support from JetBrains for the open-source project
Provides native deployment support through Helm charts for container orchestration
Member of NVIDIA Inception program, suggesting enhanced AI/ML capabilities and support
Integrates with Ollama LLM provider for SSH honeypot functionality, supporting models like codellama:7b
Connects to OpenAI's API for LLM honeypot functionality, supporting models like GPT-4o
Provides metrics and observability data in Prometheus format for monitoring
Beelzebub
Overview
Beelzebub is an advanced honeypot framework designed to provide a highly secure environment for detecting and analyzing cyber attacks. It offers a low code approach for easy implementation and uses AI to mimic the behavior of a high-interaction honeypot.
Key Features
Beelzebub offers a wide range of features to enhance your honeypot environment:
- Low-code configuration: YAML-based, modular service definition
- LLM integration: The LLM convincingly simulates a real system, creating high-interaction honeypot experiences, while actually maintaining low-interaction architecture for enhanced security and easy management.
- Multi-protocol support: SSH, HTTP, TCP, MCP(Detect prompt injection against LLM agents)
- Prometheus metrics & observability
- Docker & Kubernetes ready
- ELK stack ready, docs: Official ELK integration
LLM Honeypot Demo
Code Quality
We are strongly committed to maintaining high code quality in the Beelzebub project. Our development workflow includes comprehensive testing, code reviews, static analysis, and continuous integration to ensure the reliability and maintainability of the codebase.
What We Do
- Automated Testing: Both unit and integration tests are run on every pull request to catch regressions and ensure stability.
- Static Analysis: We use tools like Go Report Card and CodeQL to automatically check for code quality, style, and security issues.
- Code Coverage: Our test coverage is monitored with Codecov, and we aim for extensive coverage of all core components.
- Continuous Integration: Every commit triggers automated CI pipelines on GitHub Actions, which run all tests and quality checks.
- Code Reviews: All new contributions undergo peer review to maintain consistency and high standards across the project.
Quick Start
You can run Beelzebub via Docker, Go compiler(cross device), or Helm (Kubernetes).
Using Docker Compose
- Build the Docker images:
- Start Beelzebub in detached mode:
Using Go Compiler
- Download the necessary Go modules:
- Build the Beelzebub executable:
- Run Beelzebub:
Deploy on kubernetes cluster using helm
- Install helm
- Deploy beelzebub:
- Next release
Example Configuration
Beelzebub allows easy configuration for different services and ports. Simply create a new file for each service/port within the /configurations/services
directory.
To execute Beelzebub with your custom path, use the following command:
Here are some example configurations for different honeypot scenarios:
MCP Honeypot
Why choose an MCP Honeypot?
An MCP honeypot is a decoy tool that the agent should never invoke under normal circumstances. Integrating this strategy into your agent pipeline offers three key benefits:
- Real-time detection of guardrail bypass attempts.Instantly identify when a prompt injection attack successfully convinces the agent to invoke a restricted tool.
- Automatic collection of real attack prompts for guardrail fine-tuning. Every activation logs genuine malicious prompts, enabling continuous improvement of your filtering mechanisms.
- Continuous monitoring of attack trends through key metrics (HAR, TPR, MTP). Track exploit frequency and system resilience using objective, actionable measurements.
Example MCP Honeypot Configuration
Invoke remotely: beelzebub/mcp (Streamable HTTPServer).
HTTP Honeypot
HTTP Honeypot
SSH Honeypot
Follow a SSH LLM Honeypot using OpenAI as provider LLM:
Examples with local Ollama instance using model codellama:7b:
Example with custom prompt:
Testing
Maintaining excellent code quality is essential for security-focused projects like Beelzebub. We welcome all contributors who share our commitment to robust, readable, and reliable code!
Unit Tests
For contributor, we have a comprehensive suite of unit/integration tests that cover the core functionality of Beelzebub. To run the unit tests, use the following command:
Integration Tests
To run integration tests:
Roadmap
Our future plans for Beelzebub include developing it into a robust PaaS platform.
Contributing
The Beelzebub team welcomes contributions and project participation. Whether you want to report bugs, contribute new features, or have any questions, please refer to our Contributor Guide for detailed information. We encourage all participants and maintainers to adhere to our Code of Conduct and foster a supportive and respectful community.
Happy hacking!
License
Beelzebub is licensed under the MIT License.
Beelzebub is a member of NVIDIA Inception
Supported by
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Description: Introduce Beelzebub, an MCP‑based honeypot framework that enables creating decoy tools to detect prompt injection and malicious agent behavior.
Motivation: Strengthen the security of LLM workflows by adding a non‑intrusive detection mechanism.
Related MCP Servers
- AsecurityFlicenseAqualityA TypeScript-based MCP server that enables LLM agents to interact with Gel databases through natural language, providing tools to learn database schemas, validate and execute EdgeQL queries.Last updated -2310TypeScript
- -securityAlicense-qualityA Model Context Protocol server that provides enhanced browser automation capabilities using Puppeteer-Extra with Stealth Plugin, enabling LLMs to interact with web pages in a way that better emulates human behavior and avoids detection as automation.Last updated -2TypeScriptMIT License
- -securityFlicense-qualityBloodHound-MCP-AI is integration that connects BloodHound with AI through Model Context Protocol, allowing security professionals to analyze Active Directory attack paths using natural language instead of complex Cypher queries.Last updated -245Python
- -securityFlicense-qualityAn MCP server that integrates various penetration testing tools, enabling security professionals to perform reconnaissance, vulnerability scanning, and API testing through natural language commands in compatible LLM clients like Claude Desktop.Last updated -3Python