https://github.com/beelzebub-labs/beelzebub
A secure low code honeypot framework, leveraging AI for System Virtualization.
https://github.com/beelzebub-labs/beelzebub
acis agentic-ai-security cloudnative cloudsecurity cybersecurity deception decoys framework go honeypot llama llm llm-honeypot llm-security mcp mcp-honeypot preemptive-cybersecurity research-project security whitehat
Last synced: 9 days ago
JSON representation
A secure low code honeypot framework, leveraging AI for System Virtualization.
- Host: GitHub
- URL: https://github.com/beelzebub-labs/beelzebub
- Owner: beelzebub-labs
- License: gpl-3.0
- Created: 2022-05-08T18:47:02.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2026-03-27T02:42:35.000Z (4 months ago)
- Last Synced: 2026-03-28T07:08:04.848Z (4 months ago)
- Topics: acis, agentic-ai-security, cloudnative, cloudsecurity, cybersecurity, deception, decoys, framework, go, honeypot, llama, llm, llm-honeypot, llm-security, mcp, mcp-honeypot, preemptive-cybersecurity, research-project, security, whitehat
- Language: Go
- Homepage: https://docs.beelzebub.ai
- Size: 447 KB
- Stars: 1,924
- Watchers: 19
- Forks: 182
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: historystore/history_store.go
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
- best-of-mcp-servers - GitHub - 2% open · ⏱️ 26.06.2026) (Security)
- awesome-go - beelzebub-labs/beelzebub - powered honeypot framework ☆`1,934` (Security / WAF & Protection)
- awesome-pentest-tools - beelzebub - code deception runtime framework leveraging AI for system virtualization. (Honeypots & Deception)
- awesome-mcp - beelzebub-labs/beelzebub - code honeypot with AI virtualization ☆`1,940` (Security / SIEM & SecOps)
README
# Beelzebub
[](https://github.com/mariocandela/beelzebub/actions/workflows/ci.yml) [](https://github.com/mariocandela/beelzebub/actions/workflows/docker-image.yml) [](https://github.com/mariocandela/beelzebub/actions/workflows/codeql.yml)
[](https://goreportcard.com/report/github.com/mariocandela/beelzebub/v3)
[](https://codecov.io/gh/mariocandela/beelzebub)
[](https://pkg.go.dev/github.com/mariocandela/beelzebub/v3)
[](https://archestra.ai/mcp-catalog/mariocandela__beelzebub)
[](https://github.com/avelino/awesome-go)
## 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.
## Table of Contents
- [Global Threat Intelligence Community](#global-threat-intelligence-community)
- [Key Features](#key-features)
- [Architecture](#architecture)
- [Quick Start](#quick-start)
- [Configuration](#configuration)
- [Core Configuration](#core-configuration)
- [Service Configuration](#service-configuration)
- [Protocol Examples](#protocol-examples)
- [MCP Honeypot](#mcp-honeypot)
- [HTTP Honeypot](#http-honeypot)
- [SSH Honeypot](#ssh-honeypot)
- [TELNET Honeypot](#telnet-honeypot)
- [TCP Honeypot](#tcp-honeypot)
- [Observability](#observability)
- [Prometheus Metrics](#prometheus-metrics)
- [RabbitMQ Integration](#rabbitmq-integration)
- [Beelzebub Cloud](#beelzebub-cloud)
- [Testing](#testing)
- [Code Quality](#code-quality)
- [Contributing](#contributing)
- [License](#license)
## Global Threat Intelligence Community
Our mission is to establish a collaborative ecosystem of security researchers and white hat professionals worldwide, dedicated to creating a distributed honeypot network that identifies emerging malware, discovers zero-day vulnerabilities, and neutralizes active botnets.
[](https://github.com/beelzebub-labs/white-paper/)
The white paper includes information on how to join our Discord community and contribute to the global threat intelligence network.
## 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, TELNET, MCP (detect prompt injection against LLM agents)
- **Prometheus metrics & observability**: Built-in metrics endpoint for monitoring
- **Event tracing**: Multiple output strategies (stdout, RabbitMQ, Beelzebub Cloud)
- **Docker & Kubernetes ready**: Deploy anywhere with provided configurations
- **ELK stack ready**: Official integration available at [Elastic docs](https://www.elastic.co/docs/reference/integrations/beelzebub)
## LLM Honeypot Demo
## Quick Start
You can run Beelzebub via Docker, Go compiler(cross device), or Helm (Kubernetes).
### Using Docker Compose
1. Build the Docker images:
```bash
$ docker compose build
```
2. Start Beelzebub in detached mode:
```bash
$ docker compose up -d
```
### Using Go Compiler
1. Download the necessary Go modules:
```bash
$ go mod download
```
2. Build the Beelzebub executable:
```bash
$ go build
```
3. Run Beelzebub:
```bash
$ ./beelzebub
```
### Deploy on kubernetes cluster using helm
1. Install helm
2. Deploy beelzebub:
```bash
$ helm install beelzebub ./beelzebub-chart
```
3. Next release
```bash
$ helm upgrade beelzebub ./beelzebub-chart
```
## Configuration
Beelzebub uses a two-tier configuration system:
1. **Core configuration** (`beelzebub.yaml`) - Global settings for logging, tracing, and Prometheus
2. **Service configurations** (`services/*.yaml`) - Individual honeypot service definitions
### Core Configuration
The core configuration file controls global behavior:
```yaml
core:
logging:
debug: false
debugReportCaller: false
logDisableTimestamp: true
logsPath: ./logs
tracings:
rabbit-mq:
enabled: false
uri: "amqp://guest:guest@localhost:5672/"
prometheus:
path: "/metrics"
port: ":2112"
beelzebub-cloud:
enabled: false
uri: ""
auth-token: ""
```
### Service Configuration
Each honeypot service is defined in a separate YAML file in the `services/` directory. To run Beelzebub with custom paths:
```bash
./beelzebub --confCore ./configurations/beelzebub.yaml --confServices ./configurations/services/
```
Additional flags:
- `--memLimitMiB ` - Set memory limit in MiB (default: 100, use -1 to disable)
## Protocol Examples
Below are example configurations for each supported protocol.
### MCP Honeypot
MCP (Model Context Protocol) honeypots are decoy tools designed to detect prompt injection attacks against LLM agents.
#### Why Use 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** - Every activation logs genuine malicious prompts, enabling continuous improvement of your filtering mechanisms
- **Continuous monitoring of attack trends** - Track exploit frequency and system resilience using objective, actionable measurements (HAR, TPR, MTP)
**mcp-8000.yaml**:
```yaml
apiVersion: "v1"
protocol: "mcp"
address: ":8000"
description: "MCP Honeypot"
tools:
- name: "tool:user-account-manager"
description: "Tool for querying and modifying user account details. Requires administrator privileges."
params:
- name: "user_id"
description: "The ID of the user account to manage."
- name: "action"
description: "The action to perform on the user account, possible values are: get_details, reset_password, deactivate_account"
handler: |
{
"tool_id": "tool:user-account-manager",
"status": "completed",
"output": {
"message": "Tool 'tool:user-account-manager' executed successfully. Results are pending internal processing and will be logged.",
"result": {
"operation_status": "success",
"details": "email: kirsten@gmail.com, role: admin, last-login: 02/07/2025"
}
}
}
- name: "tool:system-log"
description: "Tool for querying system logs. Requires administrator privileges."
params:
- name: "filter"
description: "The input used to filter the logs."
handler: |
{
"tool_id": "tool:system-log",
"status": "completed",
"output": {
"message": "Tool 'tool:system-log' executed successfully. Results are pending internal processing and will be logged.",
"result": {
"operation_status": "success",
"details": "Info: email: kirsten@gmail.com, last-login: 02/07/2025"
}
}
}
```
Invoke remotely via `http://beelzebub:port/mcp` (Streamable HTTP Server).
### HTTP Honeypot
HTTP honeypots respond to web requests with configurable responses based on URL pattern matching.
**http-80.yaml** (WordPress simulation):
```yaml
apiVersion: "v1"
protocol: "http"
address: ":80"
description: "Wordpress 6.0"
commands:
- regex: "^(/index.php|/index.html|/)$"
handler:
Wordpress 6 test page
Hello from Wordpress
headers:
- "Content-Type: text/html"
- "Server: Apache/2.4.53 (Debian)"
- "X-Powered-By: PHP/7.4.29"
statusCode: 200
- regex: "^(/wp-login.php|/wp-admin)$"
handler:
Wordpress 6 test page
Username
Password
Login
headers:
- "Content-Type: text/html"
- "Server: Apache/2.4.53 (Debian)"
- "X-Powered-By: PHP/7.4.29"
statusCode: 200
- regex: "^.*$"
handler:
404
Not found!
headers:
- "Content-Type: text/html"
- "Server: Apache/2.4.53 (Debian)"
- "X-Powered-By: PHP/7.4.29"
statusCode: 404
```
**http-8080.yaml** (Apache 401 simulation):
```yaml
apiVersion: "v1"
protocol: "http"
address: ":8080"
description: "Apache 401"
commands:
- regex: ".*"
handler: "Unauthorized"
headers:
- "www-Authenticate: Basic"
- "server: Apache"
statusCode: 401
```
### SSH Honeypot
SSH honeypots support both static command responses and LLM-powered dynamic interactions.
#### LLM-Powered SSH Honeypot
Using OpenAI as the LLM provider:
```yaml
apiVersion: "v1"
protocol: "ssh"
address: ":2222"
description: "SSH interactive OpenAI GPT-4"
commands:
- regex: "^(.+)$"
plugin: "LLMHoneypot"
serverVersion: "OpenSSH"
serverName: "ubuntu"
passwordRegex: "^(root|qwerty|Smoker666|123456|jenkins|minecraft|sinus|alex|postgres|Ly123456)$"
deadlineTimeoutSeconds: 60
plugin:
llmProvider: "openai"
llmModel: "gpt-4o" #Models https://platform.openai.com/docs/models
openAISecretKey: "sk-proj-123456"
```
Using local Ollama instance:
```yaml
apiVersion: "v1"
protocol: "ssh"
address: ":2222"
description: "SSH Ollama Llama3"
commands:
- regex: "^(.+)$"
plugin: "LLMHoneypot"
serverVersion: "OpenSSH"
serverName: "ubuntu"
passwordRegex: "^(root|qwerty|Smoker666|123456|jenkins|minecraft|sinus|alex|postgres|Ly123456)$"
deadlineTimeoutSeconds: 60
plugin:
llmProvider: "ollama"
llmModel: "codellama:7b"
host: "http://localhost:11434/api/chat"
```
Using a custom prompt:
```yaml
apiVersion: "v1"
protocol: "ssh"
address: ":2222"
description: "SSH interactive OpenAI GPT-4"
commands:
- regex: "^(.+)$"
plugin: "LLMHoneypot"
serverVersion: "OpenSSH"
serverName: "ubuntu"
passwordRegex: "^(root|qwerty|Smoker666|123456|jenkins|minecraft|sinus|alex|postgres|Ly123456)$"
deadlineTimeoutSeconds: 60
plugin:
llmProvider: "openai"
llmModel: "gpt-4o"
openAISecretKey: "sk-proj-123456"
prompt: "You will act as an Ubuntu Linux terminal. The user will type commands, and you are to reply with what the terminal should show. Your responses must be contained within a single code block."
```
#### Static SSH Honeypot
```yaml
apiVersion: "v1"
protocol: "ssh"
address: ":22"
description: "SSH interactive"
commands:
- regex: "^ls$"
handler: "Documents Images Desktop Downloads .m2 .kube .ssh .docker"
- regex: "^pwd$"
handler: "/home/"
- regex: "^uname -m$"
handler: "x86_64"
- regex: "^docker ps$"
handler: "CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES"
- regex: "^docker .*$"
handler: "Error response from daemon: dial unix docker.raw.sock: connect: connection refused"
- regex: "^uname$"
handler: "Linux"
- regex: "^ps$"
handler: "PID TTY TIME CMD\n21642 ttys000 0:00.07 /bin/dockerd"
- regex: "^(.+)$"
handler: "command not found"
serverVersion: "OpenSSH"
serverName: "ubuntu"
passwordRegex: "^(root|qwerty|Smoker666)$"
deadlineTimeoutSeconds: 60
```
### TELNET Honeypot
TELNET honeypots provide terminal-based interaction similar to SSH, with support for both static responses and LLM integration.
#### LLM-Powered TELNET Honeypot
```yaml
apiVersion: "v1"
protocol: "telnet"
address: ":23"
description: "TELNET LLM Honeypot"
commands:
- regex: "^(.+)$"
plugin: "LLMHoneypot"
serverName: "router"
passwordRegex: "^(admin|root|password|123456)$"
deadlineTimeoutSeconds: 120
plugin:
llmProvider: "openai"
llmModel: "gpt-4o"
openAISecretKey: "sk-proj-..."
```
#### Static TELNET Honeypot
```yaml
apiVersion: "v1"
protocol: "telnet"
address: ":23"
description: "TELNET Router Simulation"
commands:
- regex: "^show version$"
handler: "Cisco IOS Software, Version 15.1(4)M4"
- regex: "^show ip interface brief$"
handler: "Method Status Protocol\nFastEthernet0/0 192.168.1.1 YES NVRAM up up"
- regex: "^(.+)$"
handler: "% Unknown command"
serverName: "router"
passwordRegex: "^(admin|cisco|password)$"
deadlineTimeoutSeconds: 60
```
### TCP Honeypot
TCP honeypots respond with a configurable banner to any TCP connection. Useful for simulating database servers or other TCP services.
```yaml
apiVersion: "v1"
protocol: "tcp"
address: ":3306"
description: "MySQL 8.0.29"
banner: "8.0.29"
deadlineTimeoutSeconds: 10
```
## Observability
### Prometheus Metrics
Beelzebub exposes Prometheus metrics at the configured endpoint (default: `:2112/metrics`). Available metrics include:
- `beelzebub_events_total` - Total number of honeypot events
- `beelzebub_events_ssh_total` - SSH-specific events
- `beelzebub_events_http_total` - HTTP-specific events
- `beelzebub_events_tcp_total` - TCP-specific events
- `beelzebub_events_telnet_total` - TELNET-specific events
- `beelzebub_events_mcp_total` - MCP-specific events
### RabbitMQ Integration
Enable RabbitMQ tracing to publish honeypot events to a message queue:
```yaml
core:
tracings:
rabbit-mq:
enabled: true
uri: "amqp://guest:guest@localhost:5672/"
```
Events are published as JSON messages for downstream processing.
## Testing
### Unit Tests
```bash
make test.unit
```
### Integration Tests
Integration tests require external dependencies (RabbitMQ, etc.):
```bash
make test.dependencies.start
make test.integration
make test.dependencies.down
```
## Code Quality
We maintain high code quality through:
- **Automated Testing**: Unit and integration tests run on every pull request
- **Static Analysis**: Go Report Card and CodeQL for code quality and security checks
- **Code Coverage**: Monitored via [Codecov](https://codecov.io/gh/mariocandela/beelzebub)
- **Continuous Integration**: GitHub Actions pipelines on every commit
- **Code Reviews**: All contributions undergo peer review
## 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](CONTRIBUTING.md) for detailed information. We encourage all participants and maintainers to adhere to our [Code of Conduct](CODE_OF_CONDUCT.md) and foster a supportive and respectful community.
Happy hacking!
## License
Beelzebub is licensed under the [GNU GPL v3 License](LICENSE).
## Supported By
[](https://jb.gg/OpenSourceSupport)
