Explaining Jailbreaks: Structured and Interpretable Safety Assessment for Large Language Models
This work proposes an explanation-aware safety framework that augments binary harmfulness detection with structured, human-interpretable explanations capturing severity, strategies, trigger spans, ratio-nales, and derived safety factors, and introduces a human–LLM hybrid annotation and canonicaliza-tion pipeline.
Sunghee Dong, Sungwon Yi, Kangmin Bae et al.
· 0 citations