Refusal Regression Root Cause Prompt

This prompt is for safety engineers and incident responders who need to diagnose why a previously passing refusal test case has started failing. A refusal regression means the system now allows a request it should block, blocks a request it should allow, or changes its refusal tone in a way that violates policy. The prompt produces a structured root cause analysis that traces the regression to a specific change: a system prompt update, a model version upgrade, a policy document modification, or a shift in the input distribution. Use this prompt after you have confirmed a regression exists through automated test suites or production monitoring.

The ideal user has access to the failing test case, the current system prompt, the previous system prompt (if changed), the current and prior model version identifiers, the relevant policy document excerpts, and any production logs showing the regression. The prompt requires these inputs to isolate the change vector. It is designed to be run as part of an incident response workflow, not as a standalone diagnostic. Pair it with the Refusal Behavior Regression Test Prompt Template for initial detection and the Policy Regression Incident Replay Prompt for full incident reconstruction.

Do not use this prompt for initial regression detection—it assumes a confirmed regression exists. Do not use it when the failure is a one-off anomaly without a clear before/after comparison; the prompt requires a known passing baseline to perform differential analysis. Do not use it as a substitute for a full incident postmortem; it produces a technical root cause hypothesis, not a business-impact assessment or remediation plan. For high-severity safety incidents, always pair the output with human review before accepting the root cause conclusion.

This section provides a copy-ready prompt template you can paste directly into your incident analysis workflow. The template is designed to guide a model through a structured root cause analysis of a specific refusal regression event. It forces the model to consider multiple failure domains—prompt changes, model updates, policy modifications, and input distribution shifts—before synthesizing a conclusion. Replace every square-bracket placeholder with concrete data from your incident before sending the prompt to the model.

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You are a senior AI safety engineer performing a root cause analysis on a production refusal regression incident. Your task is to trace a specific failure to its most probable source and recommend a targeted fix.

## Incident Report
- **Incident ID:** [INCIDENT_ID]
- **Date/Time of Regression:** [REGRESSION_TIMESTAMP]
- **Affected Policy Category:** [POLICY_CATEGORY]
- **User Input That Should Have Been Refused:** [FAILED_INPUT]
- **Actual Model Response (Incorrect):** [ACTUAL_RESPONSE]
- **Expected Refusal Behavior:** [EXPECTED_BEHAVIOR]

## Context for Analysis
- **Current System Prompt (excerpt):** [CURRENT_SYSTEM_PROMPT_EXCERPT]
- **Previous System Prompt (excerpt, if changed):** [PREVIOUS_SYSTEM_PROMPT_EXCERPT]
- **Model Version (current):** [CURRENT_MODEL_VERSION]
- **Model Version (previous, if changed):** [PREVIOUS_MODEL_VERSION]
- **Recent Policy Document Updates:** [POLICY_UPDATE_NOTES]
- **Relevant Safety Test Results (before/after):** [SAFETY_TEST_RESULTS]

## Instructions
Analyze the incident across the following four dimensions. For each, state whether it is a likely, possible, or unlikely contributor and provide specific evidence from the context above.

1.  **Prompt Change Analysis:** Did a change to the system prompt, instructions, or few-shot examples weaken or remove the relevant refusal instruction?
2.  **Model Update Analysis:** Did a model version upgrade alter the model's sensitivity to this class of unsafe requests or its instruction-following reliability?
3.  **Policy Modification Analysis:** Did a change to the safety policy definition, scope, or examples create an ambiguity that the model resolved incorrectly?
4.  **Input Distribution Shift Analysis:** Does this user input represent a novel attack pattern, phrasing, or jailbreak technique that was not covered by existing defenses or test suites?

## Output Format
Produce a structured root cause analysis in the following JSON format:
{
  "incident_id": "[INCIDENT_ID]",
  "executive_summary": "A 2-3 sentence summary of the primary root cause and its impact.",
  "dimension_analysis": {
    "prompt_change": {
      "verdict": "likely | possible | unlikely",
      "evidence": "Specific string changes or instruction weakening observed.",
      "confidence": 0.0-1.0
    },
    "model_update": {
      "verdict": "likely | possible | unlikely",
      "evidence": "Behavioral changes noted in model release notes or comparative tests.",
      "confidence": 0.0-1.0
    },
    "policy_modification": {
      "verdict": "likely | possible | unlikely",
      "evidence": "Specific policy clause changes that introduced ambiguity.",
      "confidence": 0.0-1.0
    },
    "input_distribution_shift": {
      "verdict": "likely | possible | unlikely",
      "evidence": "Analysis of the input's novelty compared to the safety test suite.",
      "confidence": 0.0-1.0
    }
  },
  "primary_root_cause": "The single most probable cause, selected from the four dimensions.",
  "recommended_immediate_action": "A specific, actionable step to resolve the regression (e.g., revert prompt, add test case, update policy wording).",
  "recommended_preventative_measures": ["A list of 2-3 process or test improvements to prevent recurrence."]
}

To adapt this template, start by filling in the Incident Report section with the precise details of the failure. The quality of the analysis depends heavily on the specificity of the FAILED_INPUT and the ACTUAL_RESPONSE. For the Context for Analysis section, provide the most relevant excerpts, not entire documents. If a dimension is not applicable—for instance, if no model update occurred—set its evidence to "N/A" and its verdict to "unlikely". After receiving the model's output, validate the JSON structure and critically review the evidence fields. A high-confidence verdict without concrete, cited evidence is a red flag that requires human re-evaluation. This analysis should be attached to the incident ticket and reviewed by a safety team lead before the recommended action is implemented.

This prompt is designed to be invoked automatically when a refusal regression is detected in a production safety pipeline. The trigger should be a failed assertion from a regression test suite, such as the Refusal Regression Release Gate Prompt or a drift alert from the Model Behavior Drift Detection Prompt. The harness must capture the specific input that triggered the regression, the expected refusal behavior, the actual model response, and the active system prompt version at the time of failure. Without this context, the root cause analysis will be speculative rather than diagnostic.

Wire the prompt into an incident management workflow that assembles a structured [INCIDENT_CONTEXT] object before invocation. This object should include: the failing input text, the expected policy label, the actual output, the model ID and version, the system prompt hash or version tag, any retrieved context or tool outputs present during the failure, and a diff of the last known passing configuration. Pass this object as a serialized JSON block into the prompt's [INCIDENT_CONTEXT] placeholder. On the output side, parse the model's structured response and validate that the root_cause_category field matches one of the expected enum values (prompt_change, model_update, policy_modification, input_distribution_shift, tool_or_context_change, unknown). If the field is missing or invalid, retry once with a stricter output schema instruction before escalating to a human analyst.

For high-severity regressions—such as a model complying with a previously refused harmful request—do not rely solely on this automated analysis. Route the prompt's output to a human safety engineer for review, and log the full trace including the incident context, the model's root cause analysis, and the reviewer's confirmation or override. Store these records in an audit trail that links the regression to a specific prompt version, model version, and policy change. This traceability is essential for postmortems and for preventing recurrence. Avoid using this prompt as a standalone decision-maker; it is a diagnostic accelerator, not a replacement for safety judgment.