Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
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.
MCP server authentication means verifying the identity of every connecting client and tool caller, rather than relying on network location or an assumption of a trusted environment.
Securing an MCP server means enforcing real authentication and authorization, scoping credentials narrowly, validating input and output, and logging every tool call for later review.
MCP servers introduce concrete risks including weak authentication, over-broad credentials, malicious tool descriptions, unreviewed third-party code, and insufficient logging of tool calls.
MCP changes AI security by standardizing tool and resource access across models, which shifts risk from bespoke integration code to shared servers, schemas, and discovery mechanisms.
