Inverse problem solvers are mappings \(S:\mathscr {Y}\to \mathscr {X}\) , where \(\mathscr {Y}\) is the space of measurements and \(\mathscr {X}\) the space of signals we wish to recover. We propose a simple algorithm to visualize the main instability of a solver implemented within an automatic differentiation framework. We justify it through simple considerations and illustrate its behavior on a deconvolution problem solved with a neural network based reconstruction method. The proposed algorithm can be used to provide additional insights on the properties of inverse problem solvers, and can be viewed as a simple uncertainty quantification technique.