While there were already some privacy challenges organized in the domain of data sanitization, they have mainly focused on the defense side of the problem. To favor the organization of successful challenges focusing on attacks, we introduce the Snake framework that is designed to facilitate the organization of challenges dedicated to attacking existing data sanitization mechanisms. In particular, it enables to easily automate the redundant tasks that are inherent to any such challenge and exhibits the following salient features: genericity with respect to attacks, ease of use and extensibility. We propose to demonstrate the main features of the Snake framework through a specific instantiation focusing on membership inference attacks over differentially-private synthetic data generation schemes. This instance of the Snake framework is currently being used for supporting a challenge colocated with APVP 2023 (the French workshop on the protection of privacy).