Large Language Models (LLMs), such as ChatGPT and BERT, are leading a new AI heatwave due to its human-like conversations with detailed and articulate answers across many domains of knowledge. While LLMs are being quickly applied to many AI application domains, we are interested in the following question: Can safety analysis for safety-critical systems make use of LLMs? To answer, we conduct a case study of Systems Theoretic Process Analysis (STPA) on Automatic Emergency Brake (AEB) systems using ChatGPT. STPA, one of the most prevalent techniques for hazard analysis, is known to have limitations such as high complexity and subjectivity, which this paper aims to explore the use of ChatGPT to address. Specifically, three ways of incorporating ChatGPT into STPA are investigated by considering its interaction with human experts: one-off simplex interaction, recurring simplex interaction, and recurring duplex interaction. Comparative results reveal that: (i) using ChatGPT without human experts' intervention can be inadequate due to reliability and accuracy issues of LLMs; (ii) more interactions between ChatGPT and human experts may yield better results; and (iii) using ChatGPT in STPA with extra care can outperform human safety experts alone, as demonstrated by reusing an existing comparison method with baselines. In addition to making the first attempt to apply LLMs in safety analysis, this paper also identifies key challenges (e.g., trustworthiness concern of LLMs, the need of standardisation) for future research in this direction.

本文使用ChatGPT对STPA(AEB自动紧急制动系统)进行安全性分析，探究了使用大型语言模型在安全性分析中的可行性与局限性，并提出了ChatGPT与人类专家进行交互式安全性分析的三种方法，并发现更多的交互会产生更好的结果。此外，文章还在未来研究方向上提出了一些关键性的挑战。

大型语言模型时代的安全分析：以使用ChatGPT的STPA为案例研究