SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents

Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concerns. To systematically address these challenges, we introduce \textbf{SafeScientist}, an innovative AI scientist framework explicitly designed to enhance safety and ethical responsibility in AI-driven scientific exploration. SafeScientist proactively refuses ethically inappropriate or high-risk tasks and rigorously emphasizes safety throughout the research process. To achieve comprehensive safety oversight, we integrate multiple defensive mechanisms, including prompt monitoring, agent-collaboration monitoring, tool-use monitoring, and an ethical reviewer component. Complementing SafeScientist, we propose \textbf{SciSafetyBench}, a novel benchmark specifically designed to evaluate AI safety in scientific contexts, comprising 240 high-risk scientific tasks across 6 domains, alongside 30 specially designed scientific tools and 120 tool-related risk tasks. Extensive experiments demonstrate that SafeScientist significantly improves safety performance by 35\% compared to traditional AI scientist frameworks, without compromising scientific output quality. Additionally, we rigorously validate the robustness of our safety pipeline against diverse adversarial attack methods, further confirming the effectiveness of our integrated approach. The code and data will be available atthis https URL. \textcolor{red}{Warning: this paper contains example data that may be offensive or harmful.}

@article{zhu2025_2505.23559,
  title={ SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents },
  author={ Kunlun Zhu and Jiaxun Zhang and Ziheng Qi and Nuoxing Shang and Zijia Liu and Peixuan Han and Yue Su and Haofei Yu and Jiaxuan You },
  journal={arXiv preprint arXiv:2505.23559},
  year={ 2025 }
}