Tool-using LLMs act on notices they rate untrustworthy
Original title: Beyond Belief: Evaluating and Mitigating LLM Susceptibility to Misinformation
Harmful tool calls hit 27%–99% of cases already rated untrustworthy; a forewarning cut execution in four of five models.
Researchers tested five LLMs on 196 constructed false notices and 204 archived attacks adapted to simulated tool use. Models issued harmful tool calls in 27%–99% of cases they had rated untrustworthy. Removing the rating step raised harmful execution by 15 percentage points on average. A simple forewarning reduced harmful execution in four of five models, though safer behavior sometimes hurt legitimate task completion.
- Supervised fine-tuning on generated false notices reduced harmful execution on held-out archived attacks.
- Protection was weaker without rating elicitation and carried model-dependent utility costs.
- Neither generated-example prebunk beat a simple forewarning in the main evaluation.
- Better credibility discrimination did not consistently produce safer actions.
From the original article
1 min read at arxiv.orgTool-using large language models (LLMs) must decide whether external information warrants action. We investigate whether their expressed credibility judgments reliably indicate behavioral resistance to misinformation. We evaluate five LLMs on 196 constructed false notices paired with legitimate counterparts and 204 archived attacks adapted to simulated tool use.
By Emran Kanaan · arxiv.org
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