# Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models

arXiv: https://arxiv.org/abs/2403.09565 · HTML: https://mlsanity.com/paper/2403.09565/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT](https://mlsanity.com/paper/2304.01246/) (2023)
- [ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?](https://mlsanity.com/paper/2303.05382/) (2023)
- [SafetyOps](https://mlsanity.com/paper/2008.04461/) (2020)

## Built on by (most cited first, 8 of 8)

- [Engineering Safety Requirements for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2403.16289/) (2024)
- [Adopting RAG for LLM-Aided Future Vehicle Design](https://mlsanity.com/paper/2411.09590/) (2024)
- [Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices](https://mlsanity.com/paper/2506.15028/) (2025)
- [Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems](https://mlsanity.com/paper/2510.06343/) (2025)
- [From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Driving](https://mlsanity.com/paper/2605.01995/) (2026)
- [Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA](https://mlsanity.com/paper/2607.08054/) (2026)
- [Traceable LLM-Generated Hazard Scenarios for Operational Safety Analysis of Aviation Systems Using ASRS Reports](https://mlsanity.com/paper/2608.04697/) (2026)
- [From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices](https://mlsanity.com/paper/2608.12025/) (2026)

## Abstract (arXiv)

DevOps is a necessity in many industries, including the development of Autonomous Vehicles. In those settings, there are iterative activities that reduce the speed of SafetyOps cycles. One of these activities is "Hazard Analysis & Risk Assessment" (HARA), which is an essential step to start the safety requirements specification. As a potential approach to increase the speed of this step in SafetyOps, we have delved into the capabilities of Large Language Models (LLMs). Our objective is to systematically assess their potential for application in the field of safety engineering. To that end, we propose a framework to support a higher degree of automation of HARA with LLMs. Despite our endeavors to automate as much of the process as possible, expert review remains crucial to ensure the validity and correctness of the analysis results, with necessary modifications made accordingly.