LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs

Artificial Intelligence (AI) is revolutionizing scientific research, yet its growing integration into laboratory environments presents critical safety challenges. While large language models (LLMs) increasingly assist in tasks ranging from procedural guidance to autonomous experiment orchestration, an "illusion of understanding" may lead researchers to overestimate their reliability. Such overreliance is especially hazardous in high-stakes laboratory settings, where failures in hazard identification or risk assessment can result in severe accidents. To address these concerns, we propose the Laboratory Safety Benchmark (LabSafety Bench), a comprehensive framework that evaluates LLMs and vision language models (VLMs) on their ability to identify potential hazards, assess risks, and predict the consequences of unsafe actions in lab environments. LabSafety Bench comprises 765 multiple-choice questions aligned with US Occupational Safety and Health Administration (OSHA) protocols, along with 520 realistic laboratory scenarios featuring dual evaluation tasks: the Hazards Identification Test and the Consequence Identification Test, with 4090 open-ended questions in total. Evaluations across eight proprietary models, seven open-weight LLMs, and four VLMs reveal that, despite advanced performance on structured assessments, no model achieves the safety threshold required for reliable operation. None scored above 75% on the Hazards Identification Test. Moreover, while proprietary models tend to excel in multiple-choice evaluations, their performance in open-ended, real-world scenario responses is comparable to that of open-source models. These findings underscore the urgent need for specialized evaluation frameworks to ensure the safe and responsible deployment of AI in laboratory settings.

@article{zhou2025_2410.14182,
  title={ LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs },
  author={ Yujun Zhou and Jingdong Yang and Yue Huang and Kehan Guo and Zoe Emory and Bikram Ghosh and Amita Bedar and Sujay Shekar and Pin-Yu Chen and Tian Gao and Werner Geyer and Nuno Moniz and Nitesh V Chawla and Xiangliang Zhang },
  journal={arXiv preprint arXiv:2410.14182},
  year={ 2025 }
}