New GUISE Benchmark: Correction Framing and Cultural Adaptation Erode LLM Safety

A new benchmark called GUISE demonstrates that altering prompt framing and linguistic-cultural context significantly weakens large language model safety defenses. Submitted to arXiv on October 15, 2026, by Mohsinul Kabir et al., the study addresses the limitation of existing safety benchmarks that rely heavily on synthetic prompts and lack focused analysis of severe crime categories.

GUISE is built upon the UNODC International Classification of Crime for Statistical Purposes (ICCS), featuring criminal-assistance scenarios sourced from real-world cases. The research finds that framing alone drives unsafe compliance: when users ask for 'corrections' rather than making direct requests, the mean unsafe compliance rate rises from 28.9% to 66.4%, peaking at 97.3%. Furthermore, culturally adapted prompts elicit significantly more unsafe compliance than literal translations of the same requests across all four non-English languages tested (+7 to +18 points), a risk that translation-based benchmarks miss.

Notably, responses from the least-safe models carry strong Anglocentric registers, with this leakage increasing monotonically with response harmfulness. The paper is currently under review, and while the authors have published code and data for future research, the results have not yet been independently reproduced.