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Securing AI Agents with Honeypots: Introducing Beelzebub MCP

As AI agents become increasingly sophisticated and integrated into critical systems, the security challenges surrounding them grow exponentially. Traditional security measures often fall short when dealing with AI systems that can be manipulated through prompt injection, adversarial inputs, or other novel attack vectors. Today, we’re exploring an innovative approach to AI agent security: Beelzebub MCP Honeypots a deceptive defense mechanism that turns the tables on attackers.

The AI Agent Security Challenge

Modern AI agents operate with access to various tools and functions, from database queries to API calls. While this flexibility enables powerful capabilities, it also creates potential attack surfaces. An attacker who successfully manipulates an AI agent might:

  • Extract sensitive information
  • Abuse legitimate functions for malicious purposes
  • Bypass security controls through social engineering
  • Exploit the agent’s reasoning capabilities to perform unauthorized actions

Traditional security approaches like guardrails, while important, don’t address the fundamental challenge: How do you detect when an AI agent is being manipulated to act against its intended purpose?

Enter the Honeypot Strategy

Honeypots have been a cornerstone of cybersecurity for decades. These deceptive systems appear to be legitimate targets but are actually traps designed to detect, deflect, and study attacks. The Beelzebub MCP (Model Context Protocol) system adapts this proven concept specifically for AI agent security.

How Beelzebub MCP Works

The system operates on an elegantly simple principle: Create functions that should never be called under normal circumstances.

1. Dual Function Environment

The AI agent operates in an environment containing two types of functions:

  • Legitimate tools: Functions the agent should use for normal operations (e.g., get_user_info, search_audit_logs)
  • Honeypot functions: Deceptive functions that appear useful but should never be invoked

2. Behavioral Analysis

When the agent receives input (whether legitimate or malicious), it must choose which functions to use. A well-functioning agent will:

  • Use legitimate tools for normal requests
  • Ignore honeypot functions entirely
  • Complete tasks without triggering security alerts

3. Attack Detection

The moment an agent attempts to invoke a honeypot function, the system immediately:

  • Flags the incident as a potential security breach
  • Logs comprehensive attack data for analysis
  • Triggers monitoring alerts through integrated dashboards (Prometheus/Grafana or ELK stack)
  • Initiates response protocols to contain the threat

4. Human-in-the-Loop Security Enhancement

The system doesn’t just detect attacks, it enables human security experts to learn from them:

  • Human Analysis: Security professionals analyze captured prompt injection attempts to understand attack methodologies
  • Guardrail Refinement: Based on real-world attack patterns, humans manually refine and strengthen the AI agent’s guardrail prompts
  • Iterative Improvement: Each guardrail fine-tuning contributes to more robust defenses against similar future attempts

Beelzebub MCP Honeypot configuration

Below, you’ll find a sample configuration for implementing an MCP honeypot with Beelzebub. The configuration is very simple and should be self-explanatory as you read through it.

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"
          }
        }
      }

For more information, please visit our Github readme mcp-honeypot

Real-World Applications

Enterprise AI Assistants

Companies deploying AI assistants for customer service or internal operations can use Beelzebub honeypots to detect when attackers attempt to manipulate the AI into revealing confidential information or performing unauthorized actions.

Development Environments

In software development scenarios, honeypot functions might mimic dangerous operations (like database deletion or credential exposure) that a legitimate development assistant should never perform.

Research and Analysis

Security researchers can use this system to study AI manipulation techniques, building a knowledge base of attack vectors and developing countermeasures.

Advantages of the Honeypot Approach

  • Proactive Detection: Unlike reactive security measures, honeypots can detect attacks in progress, potentially before any damage occurs.

  • Low False Positives: Since honeypot functions should never be called legitimately, any invocation is a clear indicator of compromise.

  • Stealth Operation: Attackers typically don’t know honeypots exist, making them difficult to avoid or circumvent.

  • Behavioral Insights: The system provides valuable data about how attackers attempt to manipulate AI agents.

  • Adaptability: New honeypot functions can be added as threat landscapes evolve.

Implementation Considerations

Designing Convincing Honeypots

The effectiveness of honeypot functions depends on making them appear legitimate and attractive to attackers. Consider:

  • Functions that seem to provide privileged access
  • Operations that appear to bypass normal security controls
  • Tools that promise sensitive information retrieval

Integration with Existing Security Infrastructure

Beelzebub MCP should complement, not replace, existing security measures. Integration with SIEM systems, threat intelligence platforms, and incident response workflows is crucial.

ELK official Beelzebub Integration

Conclusion

The Beelzebub MCP honeypot system demonstrates how classical cybersecurity concepts can be adapted for the unique challenges of AI agent security. By leveraging deception and behavioral analysis, we can create more robust defenses against the sophisticated threats targeting AI systems.

The next time you’re designing security for an AI agent, consider: What functions should never be called? Those might just be your best defense against the unknown threats of tomorrow.

The Beelzebub team is dedicated to making the internet a better and safer place. ❤️

If you want to help us with our work, please contribute to the code or leave a star on GitHub ⭐