Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation
Factual evidence
What the source reports
Researchers propose evaluating LLM safety prompts by intent recovery to distinguish true model refusals from simple task comprehension failures.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 9 October 2026
- Collected by OneBench
- 10 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.11766v1 Announce Type: new Abstract: Safety evaluations of large language models commonly summarize harmful-output behavior with attack success rate (ASR). Yet the same non-harmful outcome…
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OneBench interpretation
Institutional assessment
So what
Standard attack success metrics can mask whether a model refuses a harmful prompt or simply fails to understand the obfuscated request.
Do what
Review internal red-teaming methodologies with model risk teams to ensure intent recovery is measured alongside attack success rates.