A Framework for Quantifying Safety-Critical Threat Scenarios using Bayesian Network

Safety and cybersecurity are often addressed separately in system design, despite their increasing interdependence. This paper presents a quantitative framework for assessing safety-critical threat scenarios during early design stages. Based on Bayesian networks, the approach models attack propagation and system failures, enabling engineers to evaluate the likelihood of compromise and safety impacts across design alternatives. A key feature is dynamic updating: changes such as component failures or compromises can be introduced to reflect evolving system states and immediately update risk assessments. The framework integrates naturally with model-based systems engineering by providing a practical tool for identifying high-risk paths and, ultimately, allows for designs that are safer and more secure.