Expected Harm: Rethinking Safety Evaluation of (Mis)Aligned LLMs
Researchers argue that severity-only LLM safety metrics conflate model output with real-world impact, proposing an expected-harm framework that could reshape how enterprises validate AI guardrails.
Summary written by editorial AI · Source link below
arXiv:2602.01600v2 Announce Type: replace Abstract: Current evaluations of LLM safety predominantly rely on severity-based taxonomies to assess the harmfulness of models' responses to malicious queries. We argue that this formulation requires re-examination as it implicitly equates model output with realized harm while neglecting Execution Likelihood-the conditional probability of a threat being realized by a real-world actor given a model's response. In this work, we introduce Expected Harm, a
Editorial Analysis
Enterprises deploying LLMs under the EU AI Act need robust safety metrics; expected-harm models offer a more nuanced risk assessment aligned with regulatory intent.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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