In late 2024, Anthropic launched a Model Context Protocol that allowed the AI systems to easily connect with external tools and data. Before MCP, developers had to manually create separate connections for each external tool, resulting in complicated and hard-to-maintain integrations. MCP addresses this by providing a universal interface that allows the AI agents to automatically identify, select, and utilize various tools based on their specific requirements.
MCP has quickly become famous in the tech industry. Big players in the AI industry like OpenAI, Microsoft, Baidu, and Cloudflare have integrated MCP support into their platforms. Many developer tools like Cursor, Replit, and JetBrains have also integrated MCP to improve the overall AI-driven workflow. What’s more, platforms like Smithery.ai and MCP.so host thousands of MCP servers offering a wide variety of functions.
MCP works with three main parts:
- MCP Host: The AI app where tasks happen, like Claude Desktop or Curson IDE. This app runs the MCP client and integrates tools and data.
- MCP Client: The middleman inside the host that manages talking to MCP servers. It sends requests, receives responses, and helps the AI decide which tools to use.
- MCP Server: Connects to outside services, APIs, or data files. It provides:
- Tools to run external services,
- Resources like files or databases,
- Prompts that are reusable templates to help the AI respond better.
When a user makes a request, it is sent to the client via the host. The client selects the appropriate tool on the server for the job. Once that is done, the response is sent back to the user. All this is done in real-time with secure communication.
However, MCP introduces several security risks and controls for pentesting:
- Tool Poisoning: Hidden harmful commands inside tool descriptions trick AI into doing dangerous actions.
- Rug Pull Attack: When a trusted server alters its code to act in a malicious way after installation.
- Malicious External Resources: They are the tools that link to damaging sites and covertly send harmful instructions.
- Server Spoofing: Attackers create bogus servers with a similar name to deceive users.
- Installer Spoofing: Attackers change software installation programs to add malware.
- Puppet Attacks: A malicious server can control some other trusted tool.
- Sandbox Escape: Attacks can exploit weaknesses in the isolation of sandboxes and thereby gain access to the system.
- Privilege Escalation: Data of the target is stolen or altered with higher access.
- Data Exfiltration: Data theft happens when confidential information is captured and sent to attackers.
- Prompt Injection: Malicious input tricks an AI model into being harmful.
- File-Based Attacks: Commands can manipulate or steal important files.
- Remote Code Execution: Attackers execute code remotely to take control of a system.
In conclusion, while MCP greatly improves how AI connects with tools and data, these security issues are serious and need attention. With strong audit and verification platforms, developers must thoroughly vet and sandbox tools, followed by users being careful with permission grants. For MCP to safely succeed, the protocol has to have security built in, and everyone must work together to keep the ecosystem safe. Only then can MCP’s full potential be trusted and realized.
This lesson focuses on what is the purpose, architecture, advantages, and security risks of MCP in simple terms. MCP simplifies and standardizes AI-tool connections, meaning no more unique connections. However, be aware of vulnerabilities that can be hijacked in this new environment.
References and Readings:
- MCP Introduction (https://huggingface.co/learn/mcp-course/en/unit0/introduction)
- Systematic Analysis of MCP Security (https://arxiv.org/abs/2508.12538)
- Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystem (https://arxiv.org/abs/2506.02040)
- Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies (https://arxiv.org/abs/2504.08623)
- Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions (https://arxiv.org/abs/2503.23278)