AI tool integrations introduce a boundary between model-generated decisions and real capabilities. A secure design does not rely on the model to enforce permissions: the application and tool server must validate every action.
1. Map the trust boundaries
Start by documenting the model, MCP client, MCP server, tools, data sources, and external services. Identify which component can read sensitive data, perform actions, or make network requests. Draw the flow from user input to tool execution and note where authorization is enforced. Do not assume that a model's instruction hierarchy is a substitute for application-level access control.2. Review tool permissions and scope
Give each tool the minimum permissions needed for its task. Prefer narrow, task-specific operations over a general-purpose shell or filesystem tool. Separate read-only operations from actions that modify data or affect external systems. Require explicit confirmation for high-impact actions and ensure the server independently checks authorization on every request.3. Treat retrieved content as untrusted input
Tool output, documents, webpages, repository files, and issue comments can contain instructions intended to manipulate the model. Treat these as data, not trusted policy. Test whether malicious content can persuade the assistant to disclose unrelated context, invoke an unintended tool, or bypass a confirmation step. Keep system policy and tool authorization outside the control of retrieved text.4. Protect credentials and sensitive data
Avoid putting API keys, access tokens, passwords, or unnecessary personal data into prompts, logs, tool results, or generated reports. Store credentials in an appropriate secret manager or protected environment, scope them narrowly, and rotate them when exposure is suspected. Review logs for accidental secret capture and define retention rules before collecting test traces.5. Validate inputs and outputs
Validate arguments on the server side, including paths, identifiers, URLs, file types, and allowed operation names. Apply allowlists where practical, normalize paths before access checks, and prevent a tool from being tricked into reading outside its intended directory or contacting arbitrary hosts. Treat model-generated arguments as untrusted and validate output before passing it to another component.6. Test authorization failures deliberately
In an isolated test environment, verify that a user cannot access another user's resources by changing an identifier, that read-only roles cannot invoke write operations, and that revoked permissions take effect. Include negative tests for missing, malformed, expired, and over-scoped credentials. Record expected behavior and evidence without using real production data.7. Check auditability and failure behavior
Log the identity or session, tool name, authorization decision, and outcome where appropriate, while avoiding sensitive payloads. Errors should explain enough to debug but should not leak secrets, filesystem paths, or internal prompts. Test timeouts, unavailable servers, malformed responses, and partial failures; the safe outcome should be explicit rather than silently retrying a high-impact action.8. A compact release checklist
- Tools are documented and have minimal permissions.
- Authorization is enforced by the server, not only by model instructions.
- Untrusted content cannot grant itself authority.
- Secrets are scoped, protected, and excluded from routine logs.
- Arguments are validated and resource access is constrained.
- Negative tests cover cross-user access and prohibited actions.
- High-impact actions require appropriate confirmation.
- Audit records are useful without retaining unnecessary sensitive content.
- Failure modes are tested in an isolated environment.
This checklist is a starting point, not a certification or guarantee of security. Test only MCP servers and integrations you own or are explicitly authorized to assess.