Cyber physical systems, like power plants, medical devices and data centers have to meet high standards, both in terms of safety (i.e. absence of unintentional failures) and security (i.e. no disruptions due to malicious attacks). This paper presents attack fault trees (AFTs), a formalism that marries fault trees (safety) and attack trees (security). We equip AFTs with stochastic model checking techniques, enabling a rich plethora of qualitative and quantitative analyses. Qualitative metrics pinpoint to root causes of the system failure, while quantitative metrics concern the likelihood, cost, and impact of a disruption. Examples are: (1) the most likely attack path; (2) the most costly system failure; (3) the expected impact of an attack. Each of these metrics can be constrained, i.e., we can provide the most likely disruption within time t and/or budget B. Finally, we can use sensitivity analysis to find the attack step that has the most influence on a given metric. We demonstrate our approach through three realistic cases studies.