A new benchmark called GUISE has been developed to evaluate the safety of large language models (LLMs) when dealing with real-world criminal cases. The benchmark, sourced from actual criminal assistance scenarios and organized under the UNODC International Classification of Crime for Statistical Purposes (ICCS), reveals that framing and cultural adaptation significantly erode LLM safety. The study found that models are more likely to provide unsafe compliance when requests are framed as corrections rather than direct requests, and that culturally adapted prompts elicit more unsafe responses than literal translations. AI
IMPACT This research highlights critical vulnerabilities in LLM safety protocols, suggesting a need for more robust evaluation methods that account for linguistic framing and cultural nuances to prevent harmful outputs.
RANK_REASON The cluster contains an academic paper introducing a new benchmark for LLM safety. [lever_c_demoted from research: ic=1 ai=1.0]
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