Tool Misuse and Unauthorized Action Detection Prompt

This prompt is designed for platform engineers and trust and safety teams who need to detect when an AI agent attempts to call a tool it is not authorized to use, passes arguments that exceed its permission boundaries, or tries to execute an action that has been explicitly blocked by policy. The core job-to-be-done is creating a structured, machine-readable alert that can be consumed by a logging system, an escalation queue, or a real-time interceptor. The ideal user is someone integrating this prompt into an agent harness where tool execution is gated by a policy matrix. Required context includes the agent's full proposed tool call (function name and arguments), the user's role and permissions, and the defined tool authorization policy. Without this context, the prompt cannot make a reliable determination.

Use this prompt as a post-hoc audit step to review agent logs for policy violations, or as a real-time guardrail interceptor that runs before a tool execution function is called in your code. It is not a replacement for application-level authorization logic. Your code must still enforce permissions at the API gateway or tool execution layer. This prompt is a detection and evidence-generation layer that catches what your hardcoded rules might miss, such as subtle boundary violations or emergent misuse patterns. It is most effective when wired into a workflow that can block execution, log the alert, and optionally escalate for human review. Do not use this prompt as your sole defense against unauthorized actions; it is a safety net, not the primary lock.

Avoid using this prompt for real-time decision-making in latency-critical paths where a model inference round-trip would degrade user experience. It is also inappropriate for environments without a clearly defined tool policy matrix, as the model will lack the necessary grounding to make consistent decisions. Before deploying, ensure you have a human review process ready to handle the alerts this prompt generates. The next step after understanding this use case is to copy the prompt template, adapt the placeholders to your specific tool and policy schemas, and build a validation harness that checks the output structure before acting on it.

This prompt template acts as a guardrail interceptor for agent systems with tool access. It evaluates a proposed tool call against a defined policy matrix and returns a structured misuse alert when the agent attempts unauthorized actions, exceeds permission boundaries, or executes blocked operations. The template is designed to be placed between the agent's tool selection step and the actual tool execution, creating a blocking checkpoint that prevents policy violations before they occur.

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You are a tool-use policy enforcement guard. Your job is to evaluate a proposed tool call against a strict tool-use policy matrix and determine whether the action is authorized, requires human approval, or is blocked.

## TOOL-USE POLICY MATRIX
[TOOL_POLICY_MATRIX]

## PROPOSED TOOL CALL
Tool Name: [TOOL_NAME]
Arguments: [TOOL_ARGUMENTS]

## AGENT CONTEXT
Current Task: [AGENT_TASK]
Prior Actions Taken: [PRIOR_ACTIONS]
User Role: [USER_ROLE]
Session Risk Level: [RISK_LEVEL]

## INSTRUCTIONS
1. Map the proposed tool call to the policy matrix entries that apply.
2. Classify the action as one of: AUTHORIZED, REQUIRES_APPROVAL, or BLOCKED.
3. If REQUIRES_APPROVAL or BLOCKED, produce a structured misuse alert.
4. If AUTHORIZED, return a clean authorization record with no alert.

## OUTPUT SCHEMA
Return a JSON object with this exact structure:
{
  "classification": "AUTHORIZED | REQUIRES_APPROVAL | BLOCKED",
  "matched_policy_ids": ["string"],
  "misuse_alert": null | {
    "alert_id": "string",
    "severity": "LOW | MEDIUM | HIGH | CRITICAL",
    "violation_type": "PERMISSION_BOUNDARY | BLOCKED_ACTION | SCOPE_EXCEEDED | RATE_LIMIT | UNTRUSTED_TARGET | POLICY_GAP",
    "violation_summary": "string",
    "policy_citation": "string",
    "evidence": {
      "tool_name": "string",
      "blocked_arguments": ["string"],
      "permission_required": ["string"],
      "current_permissions": ["string"]
    },
    "recommended_action": "BLOCK_AND_ESCALATE | REQUEST_APPROVAL | LOG_AND_ALLOW | REDACT_ARGUMENTS",
    "escalation_context": "string"
  },
  "authorization_record": null | {
    "policy_ids_satisfied": ["string"],
    "permissions_checked": ["string"],
    "timestamp": "ISO8601"
  }
}

## CONSTRAINTS
- Never authorize a tool call that matches a BLOCKED policy entry.
- If multiple policies apply, use the most restrictive classification.
- When REQUIRES_APPROVAL, include exactly which permissions are missing.
- For CRITICAL severity violations, the recommended_action must be BLOCK_AND_ESCALATE.
- If the tool or arguments are not covered by any policy entry, classify as BLOCKED with violation_type POLICY_GAP.
- Do not execute the tool call. Only evaluate it.

To adapt this prompt, replace the square-bracket placeholders with your application's runtime values. The [TOOL_POLICY_MATRIX] is the most critical input—it should be a structured definition of allowed tools, permitted argument ranges, role-based permissions, rate limits, and blocked actions. Format this matrix as a JSON or YAML object that maps tool names to their authorization rules. For high-risk deployments, add a [CONSTRAINTS] section that includes domain-specific blocking rules, such as financial thresholds or PII access restrictions. Always validate the output against the schema before allowing the tool call to proceed, and log every classification result for audit purposes.

Integrating this prompt into an agent platform requires a pre-execution interception layer, not a post-hoc audit. The prompt should sit inside a tool-use gateway that evaluates every proposed tool call before the agent's runtime executes it. This means the harness must capture the agent's intended tool name, arguments, and the current conversation context, then pass them through the prompt for a structured misuse verdict. The prompt's output—a JSON block containing violation_detected, violation_type, severity, policy_reference, and evidence—becomes the decision signal for the gateway. If violation_detected is true, the gateway must block the tool call, log the full violation record, and either escalate to a human review queue or return a controlled refusal to the agent. Do not rely on the model's refusal alone; the harness must enforce the block at the application layer.

Validation is critical because a malformed JSON output from the prompt could allow a blocked action to slip through. Implement a strict schema validator that checks for all required fields and rejects any response that fails to parse. If validation fails, retry the prompt once with a stronger instruction to return valid JSON. If the retry also fails, default to blocking the tool call and logging the failure as an UNKNOWN severity event for human review. For high-throughput systems, consider running the detection prompt on a fast, cost-efficient model for initial screening, then routing ambiguous cases (where severity is MEDIUM or HIGH but confidence is below a threshold) to a more capable model for a second pass. Log every verdict—including violation_detected: false outcomes—to build an audit trail and enable false positive analysis over time.

The tool policy matrix that the prompt references must be maintained as a versioned configuration artifact, not hardcoded into the prompt text. Store your allowed tools, blocked actions, permission boundaries, and approval requirements in a structured format (YAML or JSON) and inject it into the prompt's [TOOL_POLICY] placeholder at runtime. When the policy changes, update the configuration and increment the version; the prompt itself remains stable. Before deploying any policy change, run the prompt against a golden test set of known misuse examples and benign tool calls to verify that the new policy does not introduce regressions in detection accuracy. Finally, ensure that the human review queue receiving escalations includes the full evidence payload, the blocked tool call details, and a link to the relevant policy version so reviewers can adjudicate quickly without reconstructing context.