Cyber-Physical Systems (CPSs) complexity has been continuously increasing to support new life-impacting applications, such as Internet of Things (IoT) devices or Industrial Control Systems (ICSs). These characteristics introduce new critical security challenges to both industrial practitioners and academics. This work investigates how Model-Based System Engineering (MBSE) and attack graph approaches could be leveraged to model secure Cyber-Physical System solutions and identify high-impact attacks early in the system development life cycle. To achieve this, we propose a new framework that comprises (1) an easily adoptable modeling paradigm for Cyber-Physical System representation, (2) an attack-graph-based solution for Cyber-Physical System automatic quantitative security analysis, based on the MulVAL security tool, (3) a set of Model-To-Text (MTT) transformation rules to bridge the gap between SysML and MulVAL. We illustrated the validity of our proposed framework through an autonomous ventilation system example. A Denial of Service (DoS) attack targeting an industrial communication protocol was identified and displayed as attack graphs. In future work, we intend to connect the approach to dynamic security databases for automatic countermeasure selection.