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

• URL: http://arxiv.org/abs/2402.10753v1
• Date: Fri, 16 Feb 2024 15:19:46 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. $ToolSword$ is a framework dedicated to investigating safety issues linked to LLMs in tool learning. Experiments conducted on 11 open-source and closed-source LLMs reveal enduring safety challenges in tool learning.
• Score: 46.86723087688694
• 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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