Beelzebub

Beelzebub is a next‑generation honeypot framework that blends low‑code configuration with AI‑driven interaction. It tackles the core challenge of modern threat hunting: creating realistic, high‑interaction environments that are safe to deploy and easy to manage. By emulating the behavior of a genuine system through large language models (LLMs), Beelzebub allows security teams to observe attacker tactics, techniques, and procedures (TTPs) without exposing critical infrastructure. The result is a powerful tool for identifying zero‑day exploits, monitoring botnet activity, and gathering actionable intelligence.

At its heart, Beelzebub offers a modular, YAML‑based definition of services. This low‑code approach means that operators can spin up SSH, HTTP, TCP, or even MCP listeners in minutes without deep networking expertise. The framework’s LLM integration is a standout feature: the model convincingly simulates system responses, providing attackers with an authentic experience while keeping the underlying architecture low‑interaction. This hybrid design keeps the honeypot secure, reduces false positives, and simplifies maintenance.

Key capabilities include multi‑protocol support (SSH, HTTP, TCP, MCP), built‑in Prometheus metrics for observability, and seamless integration with Docker, Kubernetes, and the ELK stack. The MCP support specifically enables detection of prompt injection attacks against LLM agents, giving researchers a unique lens into emerging AI‑centric threats. Additionally, Beelzebub’s observability stack allows real‑time monitoring of attacker actions and automated alerting, making it a practical addition to any security operations center.

Real‑world use cases span from academic research and threat intelligence communities—where distributed honeypot networks can surface new malware families—to enterprise security teams that need to validate defensive controls without risking production assets. By providing a low‑interaction yet highly realistic environment, Beelzebub empowers analysts to capture detailed attack telemetry, refine detection rules, and feed insights back into broader threat‑intel ecosystems.

Integrating Beelzebub into AI workflows is straightforward: the MCP server exposes resources and tools that can be queried by any LLM‑powered assistant. Developers can embed honeypot interaction prompts, retrieve collected metrics, or trigger automated responses directly from their AI agents. This tight coupling accelerates incident response cycles and enables continuous learning loops between human analysts, AI assistants, and the honeypot infrastructure.