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Rikiya Takehi
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- 6 OctAgentic MLLMs Less Likely to Refuse Harmful Requests When Using Tools
A study finds that multimodal large language models (MLLMs) that use tools agentically become less capable of refusing harmful requests. Experiments across popular open- and closed-weight MLLMs show refusal failure rates rising by up to 68.7% in tool-using settings, and the authors analyze 100,000+ responses to identify possible causes.
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