Input and Output Validation for AI Agent Components

Purpose

AI agents consume input from diverse sources — user prompts, retrieved documents, tool responses, API results, plugin outputs — and produce output that feeds into downstream systems. Without validation at every boundary, an injection in one component propagates through the entire agent workflow.

This control establishes a mandatory validation layer between every component in an agent pipeline: sanitise inputs before they reach the model, and validate outputs before they reach downstream systems or subsequent agents.

What to validate

Inputs — before reaching the model or triggering a tool call:

• Structure and type of data from external sources (JSON schema, expected fields).
• Content origin and provenance (was this data produced by a trusted source?).
• Presence of instruction-like patterns in data that should be purely informational.
• Size and complexity limits to prevent resource exhaustion through oversized inputs.

Outputs — before forwarding to downstream systems:

• Format and encoding appropriate to the consuming system (HTML-encode, SQL-escape, shell-sanitise).
• Absence of executable content where only data is expected.
• Compliance with expected response schema before the output leaves the agent boundary.
• Correlation with the original user intent (did the agent produce a response consistent with the input?).

Implementation notes

• Deploy validation as a mandatory gateway, not as an optional filter.
• Apply consistent validation rules across all tool interfaces — REST, MCP, gRPC, SDK.
• Log validation failures with full context for incident investigation.
• For agent-to-agent communication, validate outputs from the producing agent before they become inputs to the consuming agent.

MITRE ATLAS reference

MITRE ATLAS Mitigation AML.M0033 — Input and Output Validation for AI Agent Components.