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Yen-Shan Chen

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#machine learning Feb 2026

Expected Harm: Rethinking Safety Evaluation of (Mis)Aligned LLMs

This work introduces Expected Harm, a metric that weights the severity of a jailbreak by its execution likelihood, modeled as a function of execution cost, and demonstrates that this miscalibration creates a vulnerability that increases the attack success rate of existing jailbreaks by up to $2\times.

Yen-Shan Chen, Zhi Rui Tam, Cheng-Kuang Wu et al. · arXiv.org · 5 citations