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arxiv: 2308.01263 · v3 · submitted 2023-08-02 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Paul R\"ottger , Hannah Rose Kirk , Bertie Vidgen , Giuseppe Attanasio , Federico Bianchi , Dirk Hovy
Authors on Pith no claims yet

Pith reviewed 2026-05-15 06:47 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords exaggerated safetylanguage model refusalsafety test suiteprompt typesalignment trade-offsafe promptsunsafe contrasts
0
0 comments X

The pith

Large language models refuse safe prompts that resemble unsafe requests.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces XSTest to detect when language models refuse clearly safe prompts due to overly broad safety training. It supplies 250 safe prompts across ten types that models should answer and 200 unsafe contrast prompts that models should refuse. Testing current models with the suite shows systematic over-refusal on safe inputs that mention sensitive topics or use similar wording to harmful ones. A reader would care because this reveals a practical limit on how helpful models can be while remaining harmless.

Core claim

XSTest comprises 250 safe prompts across ten prompt types that well-calibrated models should not refuse to comply with, and 200 unsafe prompts as contrasts that models should refuse, and applying the suite to state-of-the-art models highlights systematic exaggerated safety failure modes.

What carries the argument

The XSTest suite of 450 prompts that contrasts safe inputs models should answer against unsafe inputs they should refuse.

If this is right

  • Safety training that produces refusals on safe prompts reduces model helpfulness on legitimate queries about sensitive topics.
  • Developers can run XSTest to measure and adjust the balance between harmlessness and compliance in their models.
  • The ten prompt types reveal specific patterns, such as mention of weapons or drugs, where over-refusal occurs most often.
  • General challenges remain in creating safety methods that avoid blocking safe uses of similar language.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future safety methods could incorporate explicit checks for user intent rather than surface language patterns.
  • Expanding the suite to additional languages or real-world query distributions would test whether the observed patterns generalize.
  • Over-refusal may reduce user adoption of models for everyday tasks that touch on regulated or sensitive subjects.

Load-bearing premise

The 250 prompts labeled safe are in fact unambiguously safe, so any refusal on them indicates exaggerated safety rather than ambiguity or capability limits.

What would settle it

If models answer all 250 safe prompts without refusal while refusing the 200 unsafe ones, the distinction holds; widespread refusals on the safe prompts would show the exaggerated safety pattern is real.

read the original abstract

Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful and harmless. However, there is a tension between these two objectives, since harmlessness requires models to refuse to comply with unsafe prompts, and thus not be helpful. Recent anecdotal evidence suggests that some models may have struck a poor balance, so that even clearly safe prompts are refused if they use similar language to unsafe prompts or mention sensitive topics. In this paper, we introduce a new test suite called XSTest to identify such eXaggerated Safety behaviours in a systematic way. XSTest comprises 250 safe prompts across ten prompt types that well-calibrated models should not refuse to comply with, and 200 unsafe prompts as contrasts that models, for most applications, should refuse. We describe XSTest's creation and composition, and then use the test suite to highlight systematic failure modes in state-of-the-art language models as well as more general challenges in building safer language models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces XSTest, a test suite comprising 250 safe prompts across ten prompt types that well-calibrated LLMs should comply with and 200 unsafe prompts as contrasts, to systematically identify exaggerated safety behaviors where models refuse clearly safe inputs due to over-refusal on sensitive topics or similar phrasing.

Significance. If the prompts are shown to be unambiguously safe, the test suite would offer a practical, reproducible benchmark for measuring the helpfulness-harmlessness tradeoff in LLMs and for diagnosing over-refusal failure modes in current models. The contrastive design (safe vs. unsafe) is a clear methodological strength that supports targeted evaluation.

major comments (1)
  1. [XSTest creation and composition] The section describing XSTest creation and composition states the prompt counts and types but provides no details on validation steps such as inter-annotator agreement, explicit criteria for 'unambiguous safety,' external review, or how edge-case ambiguity was excluded. This is load-bearing for the central claim, because without such evidence refusals on the 250 prompts cannot be confidently attributed to exaggerated safety rather than prompt ambiguity or capability limits.
minor comments (1)
  1. [XSTest description] Add a table or appendix listing representative prompts from each of the ten types together with their unsafe contrasts to improve reproducibility and allow readers to assess the safety judgments directly.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the detailed review of our paper on XSTest. We appreciate the recognition of the test suite's potential value and the constructive criticism regarding the description of its creation. We will revise the manuscript to provide more details on the validation steps as outlined in our point-by-point response below.

read point-by-point responses

  1. Referee: The section describing XSTest creation and composition states the prompt counts and types but provides no details on validation steps such as inter-annotator agreement, explicit criteria for 'unambiguous safety,' external review, or how edge-case ambiguity was excluded. This is load-bearing for the central claim, because without such evidence refusals on the 250 prompts cannot be confidently attributed to exaggerated safety rather than prompt ambiguity or capability limits.

    Authors: We agree that additional details on the validation process are necessary to strengthen the central claims. In the revised version of the manuscript, we will expand the section on XSTest creation and composition to explicitly describe the criteria for 'unambiguous safety' (e.g., prompts must have clear benign intent, avoid any phrasing that could reasonably be interpreted as requesting harmful content, and not involve sensitive topics in a manner that implies risk). We will also detail the review process, which involved multiple authors iteratively refining the prompts to exclude ambiguous edge cases, achieving consensus on all 250 safe prompts. While we did not perform a formal inter-annotator agreement study due to the collaborative nature of the small team, we will note this and provide examples of excluded prompts. No external review was conducted, but we will clarify that the prompts were designed based on established categories of safe vs. unsafe queries from prior safety literature. These additions will make the attribution of refusals to exaggerated safety more robust. revision: yes

Circularity Check

0 steps flagged

No circularity: XSTest construction relies on independent domain knowledge with no derivations, fits, or self-referential reductions.

full rationale

The paper introduces XSTest as a manually curated test suite of 250 safe prompts across ten types and 200 unsafe contrasts, selected from domain knowledge of safety categories. No equations, parameters, predictions, or derivations exist. Prompt selection is described as author-driven composition without reference to model outputs, fitted values, or prior results from the same authors that would create self-definition or load-bearing self-citation. The central claim (systematic identification of exaggerated safety) is supported by the suite's application to models rather than reducing to its own inputs by construction. This matches the default expectation of no significant circularity for benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the manual selection and classification of prompts into safe and unsafe categories, with no free parameters or invented entities.

axioms (1)
  • domain assumption Prompts can be reliably classified as safe or unsafe by the authors without significant ambiguity.
    The test suite construction depends on this classification to define the 250 safe prompts that models should answer.

pith-pipeline@v0.9.0 · 5511 in / 1147 out tokens · 69785 ms · 2026-05-15T06:47:08.468756+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

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