Recoverability-Controlled Safety Miscalibration in Large Language Models
Safety alignment audits that score only final LLM responses miss a subtler risk: models that correctly parse harmful intent but inconsistently refuse—a gap in current red-teaming practice.
Summary written by editorial AI · Source link below
arXiv:2610.22425v1 Announce Type: new Abstract: Safety-aligned language models are expected to refuse harmful requests while remaining useful for benign ones. However, because many evaluations score only the final response, they often conflate intent understanding with response-level safety behavior. To address this, we introduce RiskCueBench, a recoverability-controlled benchmark that separates these two stages by first measuring whether a model can successfully recover a high-level intent and
Editorial Analysis
Enterprises deploying safety-aligned LLMs may overestimate protection if evaluations do not probe the gap between intent comprehension and refusal behaviour.
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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