ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages

• URL: http://arxiv.org/abs/2402.10753v2
• Date: Fri, 16 Aug 2024 04:12:00 GMT
• Title: ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
• Authors: Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang, Songyang Gao, Yilong Wu, Qi Zhang, Tao Gui, Xuanjing Huang,
• Abstract summary: Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. To fill this gap, we present *ToolSword*, a comprehensive framework dedicated to investigating safety issues linked to LLMs in tool learning.
• Score: 45.16862486631841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes leveraging tools to augment LLMs, it frequently neglects emerging safety considerations tied to their application. To fill this gap, we present *ToolSword*, a comprehensive framework dedicated to meticulously investigating safety issues linked to LLMs in tool learning. Specifically, ToolSword delineates six safety scenarios for LLMs in tool learning, encompassing **malicious queries** and **jailbreak attacks** in the input stage, **noisy misdirection** and **risky cues** in the execution stage, and **harmful feedback** and **error conflicts** in the output stage. Experiments conducted on 11 open-source and closed-source LLMs reveal enduring safety challenges in tool learning, such as handling harmful queries, employing risky tools, and delivering detrimental feedback, which even GPT-4 is susceptible to. Moreover, we conduct further studies with the aim of fostering research on tool learning safety. The data is released in https://github.com/Junjie-Ye/ToolSword.

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