Behavior Beyond Text: Evaluating Refusal Transfer Failure in LLM Agents

arXiv cs.AIOctober 22, 2026en

arXiv:2610.28982v1 Announce Type: new Abstract: While large language models are increasingly deployed as tool-calling agents, their safety evaluations still focus primarily on their ability to refuse harmful requests in text. We investigate whether the refusal behavior in text transfers to tool use. We construct a benchmark of 2,304 prompts across four domains, consisting of matched triplets that present the same harmful or benign request in text-only, standard tool-enabled, and adversarially framed tool-enabled settings. We then assess tool-call safety using 20 predefined forbidden actions. Testing five models of varying sizes and families, we find that requests refused in text frequently r

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