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mariocandela/beelzebub

by Various

A secure low code deception runtime framework, leveraging AI for System Virtualization.

M

MCP

mariocandela/beelzebub

Added 1 June 2026

#acis #agentic-ai-security #cloudnative #cloudsecurity #cybersecurity #deception #decoys #framework

Overview

Beelzebub is a low-code deception framework written in Go. It uses AI to virtualize realistic system decoys for detecting and analyzing attacker behavior. The tool automates deployment of fake services to lure and monitor intrusions.

Best for

Best for
Security teams and red teams needing quick, AI-assisted honeypot deployments

Use cases

  • Deploy virtualized honeypots to catch reconnaissance
  • Simulate vulnerable systems for security training
  • Generate realistic decoy endpoints without manual configuration

How to use

Tools exposed

  • beelzebub_events_total
  • beelzebub_events_ssh_total
  • beelzebub_events_http_total
  • beelzebub_events_tcp_total
  • beelzebub_events_telnet_total
  • beelzebub_events_mcp_total

Tested with

ChatGPT

Notes

Beelzebub is a low-code deception framework written in Go. It uses AI to virtualize realistic system decoys for detecting and analyzing attacker behavior. The tool automates deployment of fake services to lure and monitor intrusions.

2,029 stars on GitHub. Last updated 2026-06-01. Licensed GPL-3.0.

Use cases

  • Deploy virtualized honeypots to catch reconnaissance
  • Simulate vulnerable systems for security training
  • Generate realistic decoy endpoints without manual configuration

Pros

  • Low-code approach reduces setup time for deception labs
  • Written in Go, offering good performance and concurrency
  • Open source with an active community (2029 stars)

Cons

  • AI virtualization may introduce latency or inaccurate behavior
  • Limited flexibility for highly custom decoy scenarios
  • Narrow focus on deception, not a general security tool

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Low-code approach reduces setup time for deception labs
  • Written in Go, offering good performance and concurrency
  • Open source with an active community (2029 stars)

Cons

  • AI virtualization may introduce latency or inaccurate behavior
  • Limited flexibility for highly custom decoy scenarios
  • Narrow focus on deception, not a general security tool
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