Who Pays More for Safety? Measuring the Disparate Cost of Safety Alignment across Languages
On the map: LLM alignment / RLHF / preference optimisation → Safety alignment and jailbreak attacks
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Abstract (arXiv)
Safety alignment helps models adhere to human values, but it often reduces response utility. We ask a critical but understudied question: Does safety alignment impose the cost equally across language groups? To answer this, we introduce a rigorous protocol to measure the utility loss imposed solely by safety alignment, which we term Safety Cost. Through direct pairwise comparisons between safety-aligned models and their unaligned counterparts, we find a systematic inequity: non-English users consistently bear a higher Safety Cost than English users. We further identify three underlying patterns. First, multiple languages lie in a double-penalty zone, experiencing both weaker safety protection and larger utility loss. Second, certain languages exhibit apparent utility gains that are in fact a consequence of safety filters failing to engage. Third, even high-resource languages pay a larger Safety Cost than English to reach the same level of safety. We show that these disparities arise from both explicit refusals and implicit qualitative differences across multiple dimensions. By accurately measuring the disparate effects of safety alignment, our findings expose a systematic disparity in current safety alignment practices.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Safety alignment and jailbreak attacks, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 42 of its 58 references that are in the map. First posted on arXiv 23 Aug 2026.
Builds on
42 of 58 references.
Closest ancestors · 8
Same direction, newest first
- 2025FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning12 May
- 2025Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable1 Mar
- 2025Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment16 Feb
- 2024Rule Based Rewards for Language Model Safety2 Nov
- 2024SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering21 Aug
- 2024Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons20 Jun
- 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deep10 Jun
- 2024The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts23 Jan
Other references · 34
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- 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
- 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
22 more
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- 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
- 2025Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teamingcited by 172
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- 2025LLMs Encode Harmfulness and Refusal Separatelycited by 75
- 2022Multi-VALUE: A Framework for Cross-Dialectal English NLPcited by 51
- 2025From Hard Refusals to Safe-Completions: Toward Output-Centric Safety Trainingcited by 49
- 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating Itcited by 42
- 2025Refusal Direction is Universal Across Safety-Aligned Languagescited by 37
- 2024P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMscited by 29
- 2024Exploring Safety-Utility Trade-Offs in Personalized Language Modelscited by 26
- 2026There Is More to Refusal in Large Language Models than a Single Directioncited by 22
- 2024Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Taskscited by 20
- 2025Rejected Dialects: Biases Against African American Language in Reward Modelscited by 20
- 2024From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguardscited by 17
- 2025LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Modelscited by 13
- 2025No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Modelscited by 8
- 2026Analyzing Bias in False Refusal Behavior of Large Language Models for Hate Speech Detoxificationcited by 1
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