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    <abstract>Large language models (LLMs) have exhibited great performance in autonomously calling various tools in external environments, leading to better problems solving and task automation capabilities. However, these external tools also amplify potential risks such as financial loss or privacy leaking with ambiguous or malicious user instructions. Compared to previous studies, which mainly assess the safety awareness of LLMs after obtaining the tool execution results (i.e., retrospective evaluation), this paper focuses on prospective ways to assess the safety of LLM tool utilization, aiming to avoid irreversible harm caused by directly executing tools. To this end, we propose SafeToolBench, the first benchmark to comprehensively assess tool utilization security in a prospective manner, covering malicious user instructions and diverse practical toolsets. Additionally, we propose a novel framework, SafeInstructTool, which aims to enhance LLMs’ awareness of tool utilization security through three perspectives (i.e., User Instruction, Tool Itself, and Joint Instruction-Tool), leading to nine detailed dimensions in total. We experiment with four LLMs using different methods, revealing that existing approaches fail to fully capture all risks in tool utilization. In contrast, our framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization.</abstract>
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%0 Conference Proceedings
%T SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs
%A Xia, Hongfei
%A Wang, Hongru
%A Liu, Zeming
%A Yu, Qian
%A Guo, Yuhang
%A Wang, Haifeng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xia-etal-2025-safetoolbench
%X Large language models (LLMs) have exhibited great performance in autonomously calling various tools in external environments, leading to better problems solving and task automation capabilities. However, these external tools also amplify potential risks such as financial loss or privacy leaking with ambiguous or malicious user instructions. Compared to previous studies, which mainly assess the safety awareness of LLMs after obtaining the tool execution results (i.e., retrospective evaluation), this paper focuses on prospective ways to assess the safety of LLM tool utilization, aiming to avoid irreversible harm caused by directly executing tools. To this end, we propose SafeToolBench, the first benchmark to comprehensively assess tool utilization security in a prospective manner, covering malicious user instructions and diverse practical toolsets. Additionally, we propose a novel framework, SafeInstructTool, which aims to enhance LLMs’ awareness of tool utilization security through three perspectives (i.e., User Instruction, Tool Itself, and Joint Instruction-Tool), leading to nine detailed dimensions in total. We experiment with four LLMs using different methods, revealing that existing approaches fail to fully capture all risks in tool utilization. In contrast, our framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization.
%U https://aclanthology.org/2025.findings-emnlp.958/
%P 17643-17660

Markdown (Informal)

[SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs](https://aclanthology.org/2025.findings-emnlp.958/) (Xia et al., Findings 2025)

• SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (Xia et al., Findings 2025)

ACL

• Hongfei Xia, Hongru Wang, Zeming Liu, Qian Yu, Yuhang Guo, and Haifeng Wang. 2025. SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 17643–17660, Suzhou, China. Association for Computational Linguistics.