  Intelligent agents powered by large language models (LLMs) have demonstrated
substantial promise in autonomously conducting experiments and facilitating
scientific discoveries across various disciplines. While their capabilities are
promising, these agents, called scientific LLM agents, also introduce novel
vulnerabilities that demand careful consideration for safety. However, there
exists a notable gap in the literature, as there has been no comprehensive
exploration of these vulnerabilities. This perspective paper fills this gap by
conducting a thorough examination of vulnerabilities in LLM-based agents within
scientific domains, shedding light on potential risks associated with their
misuse and emphasizing the need for safety measures. We begin by providing a
comprehensive overview of the potential risks inherent to scientific LLM
agents, taking into account user intent, the specific scientific domain, and
their potential impact on the external environment. Then, we delve into the
origins of these vulnerabilities and provide a scoping review of the limited
existing works. Based on our analysis, we propose a triadic framework involving
human regulation, agent alignment, and an understanding of environmental
feedback (agent regulation) to mitigate these identified risks. Furthermore, we
highlight the limitations and challenges associated with safeguarding
scientific agents and advocate for the development of improved models, robust
benchmarks, and comprehensive regulations to address these issues effectively.
