XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity
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| Authors | Dasol Choi et al. |
| Year | 2026 |
| HF Upvotes | 4 |
| arXiv | 2605.05662 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
Plain English
This paper introduces XL-SafetyBench, a benchmark with 5,500 test cases across 10 country-language pairs designed to test whether LLMs can handle both adversarial jailbreak attempts and culturally embedded sensitivities—not just universal harms. Current safety benchmarks rely on English translation and miss country-specific risks, so this work validates models against localized threats by using native speaker annotators and an LLM-assisted pipeline to generate culturally grounded test cases.
Key Engineering Insight
The critical distinction this paper makes is separating principled refusal (model correctly rejects harmful content) from comprehension failure (model doesn't understand the cultural context at all). This requires different evaluation metrics and reveals that models often fail not because they're unsafe, but because they literally don't grasp localized sensitivities—a much harder problem to solve.
Why It Matters for Engineers
If you're deploying LLMs globally or to non-English markets, you need to know whether your safety guardrails actually work across cultural contexts or just appear safe because they fail to understand regional sensitivities. This benchmark lets you catch real gaps before shipping, and it exposes that translation-based testing misses the real risks companies care about: models that seem compliant in English but behave dangerously in other markets.
Research Context
Previous safety benchmarks were English-first and assumed universal harm definitions applied everywhere—a clearly wrong assumption when content that's innocuous in one country triggers real harm in another. This work advances beyond the "translate and test" paradigm by building country-specific test suites from the ground up with native speakers, and it enables organizations to actually measure whether their global safety strategies work or just create an illusion of safety.
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