SafePyramid is a three-level benchmark showing frontier LLMs identify all violated rules in only 54.0%, 35.3%, and 12.9% of cases on L0, L1, and L2 respectively, indicating in-context policy guardrailing remains difficult.
Safetybench: Evaluating the safety of large language models
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
SafeClawBench supplies 600 staged adversarial tasks and three separate endpoints that show semantic acceptance, audit evidence, and sandbox-observed harm are distinct failure modes in tool-using LLM agents.
DDOR is a delta-debugging framework that localizes minimal refusal-triggering fragments for explainable overrefusal testing and targeted prompt repair in black-box LLMs.
TukaBench extends JailbreakBench to African languages via human translation, cultural adaptation, curated prompts, and code-switching, finding lower refusal rates for culturally grounded prompts and surfacing comprehension and judging limitations.
TSJ longitudinal simulation framework finds that short-term AI safety tests underestimate developmental risks, with early childhood and emerging adulthood as most vulnerable stages across cognitive trust and emotional dependency.
LLMs propagate misinformation more in lower-resource languages and lower-HDI countries, with input safety classifiers and retrieval-augmented fact-checking showing cross-lingual and regional gaps.
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