In this paper, we want to show that automated anticollision systems in aeronautical industry such as ACAS-Xu are vulnerable to hijacking threats in a urban environment which is less controlled than conventional airspace. Using reinforcement learning methods, we demonstrate the possibility to hijack the mission of a delivery drone equipped with the ACAS-Xu system in a simulated environment. Our objectives are first, to illustrate the security (interception) vulnerabilities of autonomous system and secondly, to enrich reinforcement learning benchmarks with a new one that comes from an industrial aeronautical application.