How do I validate and constrain the inputs an AI agent passes to its tools and APIs?

To validate and constrain inputs an AI agent passes to its tools and APIs, implement robust runtime controls, including schema validation and a tool broker, to ensure every action is verified against policy and intent. This approach helps prevent tool misuse and unsafe tool calls by mediating all tool invocations.

Here are concrete controls:

• Implement a Tool Broker/LLM Gateway: All tool calls should pass through a tool broker, which acts as a chokepoint to validate each call against the agent's identity, active intent token, and policy. This aligns with the NIST AI RMF function of Govern and helps mitigate OWASP LLM Top 10 risk LLM07: Insecure Tool Use.
• Utilize Schema Validation: Define and enforce input schemas for all tools using mechanisms like JSON Schema or Zod. The harness should validate the input against this schema before the tool's execution, preventing malformed input from reaching tool code. This is the cheapest and most effective runtime check and directly addresses LLM07: Insecure Tool Use.
• Enforce Tool Contracts: Every tool must declare its identity, execution logic, input schema, concurrency safety, read/write/destructive behavior, and permission rules. This contract ensures that the harness can make informed decisions about permissions and execution order.
• Implement Intent Re-verification: Before any consequential action, the system should re-derive whether the action falls within the declared intent, using the originally attested intent rather than the agent's current reasoning. This helps mitigate LLM03: Indirect Prompt Injection and LLM07: Insecure Tool Use by addressing goal misalignment cascades.
• Sandbox Tool Execution: For tools that execute generated code or process untrusted data, ensure they operate within properly isolated sandboxes, such as containers with strict resource limits, no host filesystem access, and limited network access. This helps prevent LLM07: Insecure Tool Use and container escape.
• Apply Policy Enforcement: Implement content policies on input and output at the LLM gateway, including PII detection and redaction, rate-limiting, and cost accounting. This contributes to the NIST AI RMF function of Govern and helps manage resource exhaustion.