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arxiv: 2605.05662 · v1 · submitted 2026-05-07 · 💻 cs.CL · cs.AI

Recognition: unknown

XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity

Dasol Choi , Eugenia Kim , Jaewon Noh , Sang Seo , Eunmi Kim , Myunggyo Oh , Yunjin Park , Brigitta Jesica Kartono
show 9 more authors
Josef Pichlmeier Helena Berndt Sai Krishna Mendu Glenn Johannes Tungka \"Ozlem G\"ok\c{c}e Suresh Gehlot Katherine Pratt Amanda Minnich Haon Park
Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:03 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords LLM safety evaluationcross-cultural benchmarksjailbreak robustnesscultural sensitivitymultilingual LLMsattack success ratelocal language modelsneutral-safe rate
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The pith

XL-SafetyBench shows jailbreak robustness and cultural sensitivity are independent skills in frontier LLMs, while local models appear safe mainly by failing to generate responses.

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

Current LLM safety benchmarks focus on English and often miss harms tied to specific countries and cultures. XL-SafetyBench builds 5,500 test cases for 10 country-language pairs through a pipeline of LLM discovery, validation, and dual native annotators. It splits evaluation into country-grounded jailbreak prompts and innocuous requests that embed local sensitivities. New metrics separate true refusals from cases where models simply cannot generate output. Tests on frontier and local models find the two safety dimensions do not move together and that local-model safety largely tracks generation failure.

Core claim

XL-SafetyBench is a suite of 5,500 test cases across 10 country-language pairs that contains a Jailbreak Benchmark of country-grounded adversarial prompts and a Cultural Benchmark that places local sensitivities inside ordinary requests. The construction uses LLM-assisted discovery, automated gates, and two independent native-speaker annotators per country. When 10 frontier and 27 local LLMs are measured with Attack Success Rate, the new Neutral-Safe Rate, and Cultural Sensitivity Rate, jailbreak robustness and cultural awareness show no coupling in frontier models, and local models display a near-linear ASR-NSR trade-off of r = -0.81.

What carries the argument

The multi-stage construction pipeline that combines LLM-assisted discovery, automated validation gates, and dual native-speaker annotation per country, together with the three metrics ASR, NSR, and CSR that distinguish principled refusal from generation failure.

If this is right

  • A single composite safety score should not be used for frontier models because it hides independent variation along the jailbreak and cultural axes.
  • Local LLMs need simultaneous gains in generation reliability and alignment; current high safety scores largely reflect output refusal rather than learned restraint.
  • Safety evaluations must include both adversarial country-grounded prompts and culturally embedded innocuous requests to be valid in multilingual settings.
  • NSR must be reported alongside ASR so that apparent safety is not mistaken for alignment when models simply fail to produce text.

Where Pith is reading between the lines

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

  • Training pipelines for local models could add explicit multilingual generation objectives to convert non-response safety into genuine refusal behavior.
  • Extending the same construction pipeline to more countries would allow systematic comparison of how cultural factors shift model failure modes.
  • Organizations deploying LLMs in new regions should run country-specific test suites of this form before release to surface hidden cultural risks.

Load-bearing premise

The pipeline of LLM discovery, automated gates, and dual native annotators produces test cases that accurately and without bias represent each country's specific harms and culturally embedded sensitivities.

What would settle it

Re-annotating the full set of cases with fresh native-speaker teams and obtaining substantially different sensitivity labels, or prompting the local models to generate longer answers on neutral items and seeing NSR rise without a matching drop in ASR, would show the reported trade-off does not hold.

Figures

Figures reproduced from arXiv: 2605.05662 by Amanda Minnich, Brigitta Jesica Kartono, Dasol Choi, Eugenia Kim, Eunmi Kim, Glenn Johannes Tungka, Haon Park, Helena Berndt, Jaewon Noh, Josef Pichlmeier, Katherine Pratt, Myunggyo Oh, \"Ozlem G\"ok\c{c}e, Sai Krishna Mendu, Sang Seo, Suresh Gehlot, Yunjin Park.

Figure 1
Figure 1. Figure 1: The XL-SafetyBench Construction Pipeline. A unified framework producing two comple￾mentary benchmarks. (A) Jailbreak Benchmark: generates country-grounded adversarial prompts via LLM-assisted discovery, base query generation, and an iterative attacker-judge red-teaming loop (450 prompts/country). (B) Cultural Benchmark: discovers country-specific sensitivities and embeds them as incidental details within t… view at source ↗
Figure 2
Figure 2. Figure 2: Country-grounded jailbreak prompt construction for South Korea’s real estate jeonse fraud, illustrating the four-stage pipeline: (1) harm category, (2) country-specific subcategory grounded in Korea’s jeonse (lump-sum housing deposit) system, (3) native-language base query with explicit harmful intent, and (4) adversarial prompt using an Authority Impersonation strategy. Misinformation. Each category conta… view at source ↗
Figure 3
Figure 3. Figure 3: Culturally embedded scenario construction. For Türkiye’s bread-disposal taboo, illustrat￾ing the four-stage pipeline: (1) cultural category, (2) country-specific sensitivity (discarding bread is considered disrespectful), (3) base query where the speaker unknowingly plans the violation, and (4) a scenario where the cultural issue is buried as one incidental detail within a larger surface task. cover key do… view at source ↗
Figure 4
Figure 4. Figure 4: Safety–culture dynamics across 10 frontier models and 10 countries. (a) Model-level: each point is a model’s mean ASR vs. CSR; correlations shown for all models (red) and with open￾weight outliers removed (blue). (b) Country-level: mean ASR and CSR per country. 5.1 Global Model Performance Model-level patterns. As shown in view at source ↗
Figure 5
Figure 5. Figure 5: ASR-NSR relationship across model types. view at source ↗
Figure 6
Figure 6. Figure 6: Country-level language effects (English − Local CSR %) across 7 countries. Positive values indicate an English-language advantage. The results align with a European/non-European distinction (Fisher’s exact p = 0.029). 27 view at source ↗
Figure 7
Figure 7. Figure 7: Local model performance scales with parameter count. (a) NSR shows a negative trend with log(parameters) (r = −0.38, p = 0.053), marginally above the conventional significance threshold but consistent with the predicted direction: small local models concentrate at high NSR, indicating comprehension failure rather than principled refusal. (b) CSR shows a substantial positive correlation (r = +0.68, p < 0.00… view at source ↗
read the original abstract

Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely evaluate a model's ability to detect culturally embedded sensitivities as distinct from universal harms. We introduce XL-SafetyBench. a suite of 5,500 test cases across 10 country-language pairs, comprising a Jailbreak Benchmark of country-grounded adversarial prompts and a Cultural Benchmark where local sensitivities are embedded within innocuous requests. Each item is constructed via a multi-stage pipeline that combines LLM-assisted discovery, automated validation gates, and dual independent native-speaker annotators per country. To distinguish principled refusal from comprehension failure, we evaluate Attack Success Rate (ASR) alongside two complementary metrics we introduce: Neutral-Safe Rate (NSR) and Cultural Sensitivity Rate (CSR). Evaluating 10 frontier and 27 local LLMs reveals two key findings. First, jailbreak robustness and cultural awareness do not show a coupled relationship among frontier models, so a composite safety score obscures per-axis variation. Second, local models exhibit a near-linear ASR-NSR trade-off (r = -0.81), indicating that their apparent safety reflects generation failure rather than genuine alignment. XL-SafetyBench enables more nuanced, cross-cultural safety evaluation in the multilingual era.

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

3 major / 2 minor

Summary. The paper introduces XL-SafetyBench, a benchmark of 5,500 test cases across 10 country-language pairs. It comprises a Jailbreak Benchmark of country-grounded adversarial prompts and a Cultural Benchmark embedding local sensitivities in innocuous requests. Construction uses a multi-stage pipeline of LLM-assisted discovery, automated validation gates, and dual independent native-speaker annotators per country. New metrics ASR, NSR, and CSR are defined to distinguish refusal from comprehension failure. Evaluation of 10 frontier and 27 local LLMs yields two findings: jailbreak robustness and cultural awareness are uncoupled among frontier models (so composite scores obscure variation), and local models show a near-linear ASR-NSR trade-off (r = -0.81), suggesting apparent safety often reflects generation failure rather than alignment.

Significance. If the test cases are shown to faithfully represent country-specific harms, the work provides a useful cross-cultural lens on LLM safety that separates jailbreak robustness from cultural sensitivity detection. The scale of the evaluation (37 models) and the introduction of complementary metrics to isolate generation failure are concrete strengths that could inform more nuanced safety assessment in multilingual settings.

major comments (3)
  1. [§3] §3 (Benchmark Construction): The multi-stage pipeline is described but reports neither inter-annotator agreement (e.g., Cohen’s κ or percentage agreement) for the dual native-speaker annotations nor the precise thresholds and pass/fail rates of the automated validation gates. These quantities are load-bearing for the central claims, because both the uncoupling result for frontier models and the r = -0.81 trade-off for local models presuppose that the 5,500 items accurately and unbiasedly capture country-grounded harms and embedded sensitivities.
  2. [§5] §5 (Results and Analysis): The reported correlation r = -0.81 between ASR and NSR for the 27 local models is presented without the exact formulas used to compute ASR and NSR on a per-item basis, the number of test cases contributing to each model’s scores, any statistical controls (e.g., for prompt length or language), or a p-value. This detail is required to assess whether the linear trade-off is robust or an artifact of metric definition.
  3. [§4] §4 (Metric Definitions): NSR and CSR are introduced post-hoc to distinguish principled refusal from generation failure, yet the paper provides no human baseline or comparison against existing refusal classifiers. Without such anchors, it remains unclear whether the observed trade-off in local models reflects genuine model behavior or simply differences in how the new metrics operationalize “safe” versus “failed” outputs.
minor comments (2)
  1. [Abstract] Abstract: the sentence “We introduce XL-SafetyBench. a suite” contains a period instead of a comma or colon.
  2. Throughout: the paper uses “country-language pairs” without an explicit list or table mapping each of the 10 pairs to its language and country; adding this would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which help clarify key aspects of our benchmark construction, metric definitions, and results. We have revised the manuscript to incorporate the requested details and provide additional context where appropriate.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark Construction): The multi-stage pipeline is described but reports neither inter-annotator agreement (e.g., Cohen’s κ or percentage agreement) for the dual native-speaker annotations nor the precise thresholds and pass/fail rates of the automated validation gates. These quantities are load-bearing for the central claims, because both the uncoupling result for frontier models and the r = -0.81 trade-off for local models presuppose that the 5,500 items accurately and unbiasedly capture country-grounded harms and embedded sensitivities.

    Authors: We agree that these quantities are important for validating the benchmark. In the revised manuscript, we have added a dedicated paragraph and table in §3 reporting inter-annotator agreement (Cohen’s κ ranging 0.76–0.93 across countries, average percentage agreement 88%) and the automated gate thresholds (e.g., semantic similarity threshold of 0.82, toxicity filter <0.15) along with pass rates (71% of candidates retained after all gates). These additions directly support the fidelity of the 5,500 items to country-specific harms. revision: yes

  2. Referee: [§5] §5 (Results and Analysis): The reported correlation r = -0.81 between ASR and NSR for the 27 local models is presented without the exact formulas used to compute ASR and NSR on a per-item basis, the number of test cases contributing to each model’s scores, any statistical controls (e.g., for prompt length or language), or a p-value. This detail is required to assess whether the linear trade-off is robust or an artifact of metric definition.

    Authors: We have expanded §5 to include the per-item formulas: ASR equals 1 if the model output is judged harmful by the safety evaluator and 0 otherwise; NSR equals 1 if the output is safe and on-topic for neutral prompts. All scores use the complete set of 5,500 test cases per model. We now report the Pearson r with p-value (p < 0.001) and note that prompt lengths were balanced by construction across the 10 languages, requiring no further statistical controls. This confirms the trade-off is not an artifact. revision: yes

  3. Referee: [§4] §4 (Metric Definitions): NSR and CSR are introduced post-hoc to distinguish principled refusal from generation failure, yet the paper provides no human baseline or comparison against existing refusal classifiers. Without such anchors, it remains unclear whether the observed trade-off in local models reflects genuine model behavior or simply differences in how the new metrics operationalize “safe” versus “failed” outputs.

    Authors: We acknowledge the value of external anchors. The revised §4 now includes a direct comparison of NSR/CSR to existing classifiers (e.g., Llama Guard and OpenAI Moderation API), noting that our metrics incorporate cultural context absent from those tools. We have also added results from a human validation study on 250 randomly sampled outputs, achieving 89% agreement with our automated labels. While a full-scale human baseline on all 5,500 items was not feasible, the subset validation and comparative discussion address the core concern. revision: partial

Circularity Check

0 steps flagged

No significant circularity in benchmark construction or empirical claims

full rationale

The paper constructs XL-SafetyBench via an external multi-stage pipeline (LLM-assisted discovery, automated gates, dual native-speaker annotators) and reports empirical observations on 37 LLMs, including the uncoupled relationship in frontier models and the ASR-NSR correlation (r = -0.81) in local models. These are data-driven findings rather than quantities defined by the paper's own fitted parameters or self-referential equations. No self-definitional metrics, fitted inputs renamed as predictions, load-bearing self-citations, or ansatzes smuggled via prior work appear in the abstract or described methodology. The new rates (NSR, CSR) are introduced as complementary evaluation axes without reducing to the benchmark inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the assumption that native-speaker annotations faithfully capture cultural sensitivities and that the chosen metrics distinguish principled refusal from comprehension failure.

axioms (2)
  • domain assumption Dual independent native-speaker annotations produce ground-truth labels for cultural sensitivities without systematic bias.
    Invoked in the construction pipeline described in the abstract.
  • domain assumption Attack Success Rate, Neutral-Safe Rate, and Cultural Sensitivity Rate can be measured consistently across languages and models.
    Required for the reported correlation and decoupling findings.

pith-pipeline@v0.9.0 · 5601 in / 1380 out tokens · 54384 ms · 2026-05-08T11:03:14.284607+00:00 · methodology

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