Tool Mediation Alters Refusal Mechanisms in Large Language Models
Tool access raises LLM refusal thresholds and makes those refusals easier to weaken than chat refusals.
The paper examines why tool-using large language models refuse harmful requests less often than in ordinary conversation, across a diverse set of open-weight models. Harmfulness remains strongly represented and transfers between conversational and tool-mediated inputs, but the two modes distribute harm-related computation differently. Chat inputs are refused at relatively low perceived harm, while tool-mediated inputs stay permissive until a substantially higher threshold. Progressively weakening the refusal computation disrupts tool-mediated refusal at lower intervention strength than conversational refusal, even when benign capabilities remain intact.
- Harmfulness stays encoded and transfers between chat and tool inputs
- Tool-mediated requests need a much higher harm level before refusal
- Weakening refusal circuits breaks tool refusal sooner than chat refusal
- Authors say ordinary safety evaluations may not transfer to LLM agents
From the original article
1 min read at arxiv.orgLarge language models (LLMs) are increasingly deployed with access to external tools, yet harmful tool-mediated interactions are less likely to be refused when compared to regular conversational ones. As this change in refusal behavior remains underexplored, we investigate its underlying mechanisms across a diverse set of open-weight language models.
By Abel Rodríguez · arxiv.org
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