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What an AI refusal is—and what it is not
A refusal is a response in which an AI assistant declines to help with a request it should not fulfill. Depending on the request and the applicable rules, the assistant might say no directly, provide a safer version of the requested information, or redirect the user toward a benign alternative. Those behaviors can all serve a safety purpose, but an automated evaluator may not label them identically.
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Refusal behavior is shaped by training and by instructions that apply while the model is answering. Training can reward the model for declining disallowed requests, while examples of allowed requests help it avoid treating every safety-adjacent subject as forbidden. At inference—the point when the model responds to a prompt—higher-priority instructions can further constrain the answer.
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How the instruction hierarchy is meant to help
AI assistants can receive instructions from multiple places. The Model Spec describes how to determine which instructions apply and how to resolve conflicts: a lower-priority instruction does not override a conflicting higher-priority one. OpenAI’s instruction-hierarchy research describes the order as system, developer, user, and then tool content.
For example, a tutor may have a developer instruction to teach a subject without simply giving a student the answer. If the student asks it to ignore that instruction and provide the answer, the assistant should follow the tutor’s higher-priority direction rather than the conflicting user request. The same principle is intended to help with safety rules: a user’s request to disregard them should not take precedence.
Instruction priority is a defense, not a guarantee. It only helps if the model recognizes the relevant instructions, identifies the conflict, and follows the higher-priority one. Training can improve performance on evaluated instruction-hierarchy attacks, but a result under tested conditions does not establish that every possible attack or deployment setting is covered.
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Three different ways a refusal can go wrong
Unsafe compliance after a jailbreak
A jailbreak is an adversarial prompt designed to circumvent a refusal and elicit disallowed content. It may try to reframe the request or combine techniques to make the prohibited goal appear acceptable to the model. The important distinction is between the harmful request and the tactic used to get past the safeguard: a jailbreak is an attempt to change the model’s response, not evidence that its rules have changed.
Whether a particular defense works depends on the prompt, model, and test conditions. A benchmark result can show how a model performed on the attacks included in that evaluation; it cannot by itself show that all jailbreak methods, future variants, or real-world contexts have been tested.
Following hostile instructions in outside content
Prompt injection is related to instruction conflict but is a distinct attack path. In a tool-using or retrieval setting, the assistant may encounter text from a webpage or another third-party source that tells it to ignore its rules or take an action. That text should be treated as data to analyze, not as a command with authority over the assistant.
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This is different from a user directly asking the model to disregard a higher-priority instruction. In the direct case, the conflicting instruction comes from the user. In an indirect attack, malicious instructions arrive embedded in external content the model is processing. OpenAI’s current evaluation discussion distinguishes direct instruction-hierarchy attacks from indirect attacks embedded in outside content. Robustness to one pathway should not be assumed to prove robustness to the other.
Refusing a harmless request
The opposite failure is overrefusal: the model declines a benign request because it resembles a prohibited one. A request may concern a safety-sensitive subject without seeking harmful instructions. If the system treats the topic alone as decisive, it can withhold useful, allowed information.
This is why safety is not measured only by whether a model blocks unsafe requests. Evaluations also include benign prompts in safety-relevant contexts and assess whether the model avoids refusing them unnecessarily. The goal is not maximal refusal; it is to refuse the disallowed request while still handling allowed requests appropriately.
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What published evaluation figures do—and do not—show
Scores are meaningful only with their model, metric, benchmark, and evaluation context attached. They are not universal safety rates, and results from different tests should not be compared as if they used the same prompts, definitions, or graders.
| Evaluation | Reported result | How to interpret it |
|---|---|---|
| OpenAI o3 and o4-mini System Card, standard refusal evaluation | Aggregate “not_overrefuse” was 0.84 for o3, 0.81 for o4-mini, and 0.86 for o1. | These are scores for that evaluation set, not population-wide success rates or a measure of every kind of safety behavior. |
| OpenAI o3 and o4-mini System Card, standard refusal evaluation | The Chain of Thought summarizer for both models scored 0.95 on “not_unsafe.” | This is the named metric in that specific evaluation, not a general claim that either model is 95% safe. |
| OpenAI’s GPT-4 Research comparison | OpenAI reported an 82% decrease in GPT-4’s tendency to respond to disallowed requests compared with GPT-3.5, and said GPT-4 responded to sensitive requests in accordance with its policies 29% more often. | These are OpenAI’s reported comparisons in its stated research context; they should not be read as universal rates across prompts or deployments. |
| OpenAI’s October 2026 GPT-6 Sol and GPT-6 Luna update | Instruction-hierarchy robustness was reported as 99.99% for GPT-6 Sol and 99.79% for GPT-6 Luna under that update’s evaluation. | The figures describe the update’s evaluation, not resistance to every form of prompt injection or every real-world attack. |
The metric name is part of the result. “Not_overrefuse” concerns a different failure mode from “not_unsafe.” A model might score well on one and still need scrutiny on the other. Likewise, a result about instruction-hierarchy robustness should not be silently treated as a score for indirect prompt injection unless the evaluation actually tests that pathway.
Why a benchmark score can mislead
An evaluation is a designed test, not an exhaustive map of every way a user or external source might interact with a model. The wording and mix of prompts, the definition of a passing answer, and the grader all affect the result. A benchmark can uncover weaknesses without proving that a model is safe outside the tested conditions.
Grading itself can fail. In a joint OpenAI–Anthropic evaluation, manual inspection found cases in which automated graders misread nuanced refusals and safe answers. A model may decline the unsafe part of a request while offering permitted help, for instance; a grader that expects a particular refusal phrase may not recognize that distinction. Conversely, a response that sounds cautious should not automatically count as safe if it still provides disallowed assistance.
For a useful reading of any reported score, look for the exact model and version, metric, test set, attack type, and rubric. Check whether benign prompts were included to measure overrefusal, whether direct and indirect attacks were evaluated separately, and whether humans reviewed borderline grader decisions. If those details are not available, treat the headline percentage as limited evidence rather than a broad guarantee.
A practical way to assess a refusal claim
When deciding what a safety result says about an AI system, ask these questions:
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- What behavior was measured? Unsafe compliance, overrefusal, direct jailbreak resistance, and resistance to hostile instructions in external content are separate questions.
- Which model and evaluation? A figure applies to the named model and test context, not automatically to other models, versions, or deployments.
- What counted as success? A rubric may treat direct refusal, safe completion, and redirection differently. The evaluation should make that clear.
- How were errors checked? Automated grading can misread nuanced answers; manual review helps identify those mistakes but does not make an evaluation exhaustive.
- What was not tested? A high result on one benchmark does not establish protection against every attack technique or deployment-specific risk.
These checks are also useful when comparing systems. Compare like with like: the same failure mode, comparable test design, and clearly described grading. If one result measures refusal of harmful prompts and another measures resistance to instruction conflicts, putting their percentages side by side does not make them directly comparable.
What to expect from a well-designed refusal
A good refusal is not simply a stock sentence. It should avoid providing the disallowed assistance, communicate the boundary clearly enough to be understood, and offer a safe alternative when one is appropriate. For a benign request near a sensitive subject, good behavior may mean answering carefully rather than refusing reflexively.
That balance is difficult to measure with one number. Safety requires limiting unsafe compliance without turning caution into blanket denial. Training, instruction priority, adversarial testing, and careful evaluation each contribute, but none alone proves that a model will handle every future interaction correctly.
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