Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
Secure error handling for MCP integrations means returning enough information for a model to recover gracefully without leaking internal details, stack traces, or data that reveals more than the caller should see.
Rate limiting MCP tools means capping how often a given identity or agent can call a tool in a given window, protecting against runaway loops, abuse, and unexpected cost as much as deliberate attacks.
Secrets management for MCP servers means storing credentials outside of code and configuration files, scoping them narrowly, and rotating them on a schedule instead of leaving them static indefinitely.
Sandboxing MCP tools means running tool execution in an isolated environment with constrained resources and network access, so a compromised or misused tool can't reach beyond its intended boundary.
Logging and auditing MCP tool calls means recording identity, parameters, results, and policy decisions for every call, so an incident can be reconstructed rather than guessed at.
Reviewing a third-party MCP server means checking its source and maintainer, the permissions it requests, its actual tool behavior, and its logging and update practices before connecting it to production.
MCP server supply chain risk comes from connecting agents to third-party servers whose code, dependencies, and update process nobody on your team controls or fully reviews.
Preventing data exfiltration through MCP means controlling which tools can send data externally, watching for unusual data movement patterns, and never letting a model's own reasoning be the only safeguard.
Output validation for MCP tools means checking data returned from a tool before it reaches the model's context, since that content can carry injected instructions or sensitive data that shouldn't flow further.
Input validation for MCP tools means checking every model-supplied parameter's type, format, and range before it executes, since tool arguments can be manipulated by prompt injection or plain model error.
Least privilege for MCP integrations means giving each server, tool, and calling identity only the narrow access it needs to do its specific job, instead of one broad credential shared across everything.
Authorization for MCP tools means checking the requesting user's actual permissions against a specific tool and its parameters at call time, not just confirming the connection was authenticated.
