Agent Tool Input Whitelisting Prompt Template

This prompt is designed for security engineers and platform developers who need to enforce allowlist-based validation on arguments before an agent invokes an external tool. It acts as a pre-execution gate that compares each argument against a defined set of permitted values, patterns, and ranges. Use this prompt when you cannot trust the agent's reasoning alone to constrain tool inputs and you need a deterministic, auditable decision before a tool call reaches your infrastructure. This is a critical control for preventing injection, data exfiltration, and unauthorized actions in agent-to-tool gateways.

Deploy this prompt when the tool being invoked has a narrow, well-defined input contract—for example, a database lookup that should only accept a fixed set of table names, an API endpoint with an enumerated set of allowed actions, or a file-system operation restricted to a specific directory. The prompt is most effective when you can predefine the exact set of allowed values, regex patterns, or numeric ranges for each argument. It is not a replacement for parameterized queries or OS-level sandboxing; rather, it is a defense-in-depth layer that catches violations before they reach those controls. You should pair this prompt with structured logging that captures every validation decision, including rejected arguments and the specific rule that failed, so that security operations teams can audit bypass attempts.

Do not use this prompt when the tool's valid input space is too large or dynamic to enumerate in an allowlist—for example, free-form search queries, natural language parameters, or user-generated content that must be passed through. In those cases, a denylist or output sanitization approach is more appropriate. Also avoid relying on this prompt as your sole security boundary; it should be one layer in a multi-layered defense that includes parameterized interfaces, least-privilege tool credentials, and runtime sandboxing. Before deploying, test the prompt against a harness of known bypass techniques including encoding tricks, case variations, Unicode homoglyphs, and boundary-value attacks to ensure the allowlist rules are not trivially circumvented.

This prompt template is designed to be the core instruction set for a validation layer that sits between an agent's decision to call a tool and the actual execution of that tool. It forces the model to act as a deterministic security gate, comparing every supplied argument against a pre-defined whitelist of allowed values, patterns, and ranges. The primary job-to-be-done is preventing injection, parameter tampering, and unsafe argument combinations before they reach a downstream API, database, or system command. The output is a strict JSON decision object that your application harness can parse to either allow the call or block it with a detailed rejection reason.

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You are a strict tool input validation engine. Your only job is to compare the provided arguments against the whitelist specification and return a JSON decision. Do not execute the tool. Do not explain your reasoning outside the JSON.

[WHITELIST_SPECIFICATION]

[TOOL_ARGUMENTS]

[OUTPUT_SCHEMA]
{
  "decision": "ALLOW" | "BLOCK",
  "blocked_arguments": [
    {
      "argument_name": "string",
      "supplied_value": "string",
      "rejection_reason": "string",
      "whitelist_rule_violated": "string"
    }
  ],
  "allowed_arguments": [
    {
      "argument_name": "string",
      "supplied_value": "string"
    }
  ]
}

[CONSTRAINTS]
- If any argument violates the whitelist, the decision must be "BLOCK".
- Rejection reasons must cite the specific whitelist rule that was violated.
- Treat all inputs as case-sensitive unless the whitelist explicitly states otherwise.
- If an argument is not defined in the whitelist, block it with the reason "Argument not in whitelist".
- Do not attempt to coerce, trim, or sanitize values. Only validate.

To adapt this template, you must replace the [WHITELIST_SPECIFICATION] and [TOOL_ARGUMENTS] placeholders with concrete data at runtime. The whitelist specification should be a structured block, likely JSON or YAML, that defines permitted arguments, their allowed values or regex patterns, and any cross-argument constraints. The tool arguments are the raw key-value pairs the agent generated. For high-risk environments, this prompt should be paired with a post-processing validation step in your application code that independently verifies the JSON structure and re-checks the decision logic before any tool is invoked. Never rely solely on the model's output for a blocking decision in a security-critical path.

The whitelisting prompt is not a standalone security control; it must be embedded in a pre-invocation gateway that intercepts every tool call before execution. In a typical implementation, the agent framework emits a tool call with arguments, and the gateway routes those arguments through the whitelisting prompt before forwarding them to the actual tool API. The prompt returns a structured decision—allowed, denied, or flagged—along with rejection reasons and canonicalized values. The gateway must enforce this decision programmatically: allowed calls proceed, denied calls are blocked with the rejection reason returned to the agent, and flagged calls are queued for human review. This architecture ensures the prompt's output is not advisory but binding, preventing an agent from bypassing the whitelist through subsequent turns or alternative tool paths.

Validation logic should operate in two layers. First, parse the prompt's JSON output and confirm it matches the expected schema: a top-level decision field with enum values, a reasons array of strings, and an optional canonical_args object. If the prompt returns malformed JSON or missing required fields, treat it as a deny decision and log the parse failure. Second, implement a retry strategy for transient model failures—one retry with exponential backoff is usually sufficient—but never retry a deny decision hoping for a different result. For high-throughput systems, consider caching whitelist decisions for identical argument patterns with a short TTL, but invalidate the cache whenever the whitelist rules or tool schema change. Log every decision with the tool name, input arguments, decision, reasons, model version, and latency to create an audit trail that supports debugging and compliance reviews.

Tool choice matters for this workflow. Use a fast, deterministic model for the whitelisting prompt—smaller variants of GPT-4, Claude, or fine-tuned open-weight models work well because the task is classification, not generation. Avoid models with high creative temperature settings; set temperature to 0 or near-zero to maximize consistency. If your gateway processes sensitive arguments, run the whitelisting model in a private deployment or VPC to prevent argument data from leaving your trust boundary. For regulated environments, pair the automated whitelisting with a human review queue for all flagged decisions and a random sample of allowed decisions to detect drift. The prompt's flagged output is your circuit breaker—never silently downgrade a flagged decision to allowed without human review, as flagged cases often represent novel bypass attempts that the whitelist rules did not anticipate.