MCP (Model Context Protocol) is the big newsworthy subject in the AI world recently. Read below to get recent OWASP released GenAI information as it applies to the added information security headaches for CISOs and Infosec managers everywhere... Godspeed!
Artificial intelligence is no longer just answering questions in a chat window; it is actively performing tasks, writing code, and interacting with our external tools. At the heart of this "agentic AI" revolution is the Model Context Protocol (MCP).
MCP servers act as the critical connective tissue between AI assistants and your organization’s APIs, tools, and data sources.
FREE DOWNLOAD: OWASP Sourced Reference
.....as we know...but with great power comes a drastically expanded attack surface.
Because MCP servers facilitate complex, multi-step actions on behalf of users, a single vulnerability here can be catastrophic. If you're building or integrating AI agents, securing your MCP infrastructure isn't just an afterthought—it's a fundamental requirement.
Let's dive into why securing MCP servers is uniquely challenging and explore actionable guidance for keeping your AI ecosystems safe.
Why MCP Servers Aren't Just "Traditional APIs"
It’s tempting to treat an MCP server like any other REST or GraphQL API you’ve built in the past. However, doing so ignores the unique mechanics of how AI assistants interact with external systems. MCP servers require a different security paradigm for a few key reasons:
Delegated User Permissions
AI agents typically act on behalf of a human user, meaning the MCP server must flawlessly manage delegated permissions without accidentally granting the AI "superuser" access to your data.
Dynamic Tool-Based Architectures
Unlike predictable API endpoints, MCP servers provide tools that Large Language Models (LLMs) can choose to invoke dynamically based on conversational context.
Chained Tool Calls
LLMs often string together multiple tool calls to achieve a goal. A malicious prompt could trick the AI into chaining calls in a way the developer never intended, bypassing logical security boundaries.
"Unlike traditional APIs, MCP servers operate with delegated user permissions, dynamic tool-based architectures, and chained tool calls, increasing the potential impact of a single vulnerability."
5 Best Practices for Secure MCP Server Development
To safely enable powerful, tool-integrated AI capabilities, platform engineers and developers need to build defense-in-depth directly into their MCP servers. Based on the latest guidance from the OWASP Gen AI Security Project, here are the five core pillars of secure MCP development:
Build a Secure-by-Design Architecture
Start by enforcing the principle of least privilege at the architectural level. Ensure your MCP server only exposes the absolute minimum set of tools and data required for the AI to do its job. Consider implementing human-in-the-loop (HITL) approval gates for any tool calls that mutate data or perform sensitive actions.
Enforce Strong Authentication & Authorization
Never trust the AI assistant to handle authorization. The MCP server itself must verify the identity of the end-user initiating the prompt and strictly enforce access controls based on that specific user's permissions, not just the service account of the AI application.
Implement Strict Data Validation
LLMs are highly susceptible to prompt injection, and they pass those injections directly down to your tools.
- Input Validation: Strictly sanitize and validate all arguments passed to your MCP tools by the LLM.
- Output Validation: Sanitize the data your MCP server returns to the LLM to prevent data exfiltration or secondary injection attacks.
Guarantee Session Isolation
In a multi-tenant environment, AI interactions must be strictly isolated. Ensure that one user's context, data, or tool executions can never bleed into another user's session. Treat every LLM interaction as a stateless, strictly scoped transaction.
Deploy in Hardened Environments
Your code is only as secure as the environment it runs in. Deploy MCP servers in isolated, hardened containers with strict network egress policies. Since MCP servers act as a bridge to internal data, restricting what internal networks the server can talk to limits the blast radius of a potential breach.
Who Needs to Pay Attention?
Securing MCP servers is a cross-functional responsibility.
- Software Architects need to design secure boundaries between the LLM and internal systems.
- Platform Engineers must provision hardened deployment environments and secure network routing.
- Development Teams are responsible for writing strict validation and authorization logic for every single tool exposed via MCP.
The Model Context Protocol is unlocking incredible new capabilities for AI, transforming passive chatbots into proactive digital assistants. However, organizations cannot afford to blindly bridge LLMs into their secure networks. By embracing secure architecture, strict validation, and robust session isolation, you can confidently reduce your risk while adopting agentic AI.
3 resources for additional context on MCP security
The official guide from the protocol's creators detailing architectural requirements, secure consent flows, and the critical differences between securing local versus remote servers.
An authoritative breakdown of the most critical security vulnerabilities facing MCP environments, including specific threat vectors like Token Mismanagement, Tool Poisoning, and Prompt Injection.
A practical, developer-focused overview of real-world risks in agentic AI—such as unauthorized command execution and "confused deputy" attacks—alongside concrete mitigation strategies.
