Runtime enforcement is a powerful technique to ensure that a program will respect a given set of properties. We extend previous work on this topic in several directions. Firstly, we propose a generic notion of enforcement monitors based on a memory device and finite sets of control states and enforcement operations. Moreover, we specify their enforcement abilities w.r.t. the general Safety-Progress classification of properties. Furthermore, we propose a systematic technique to produce a monitor from the automaton recognizing a given safety, guarantee, obligation or response property. Finally, we show that this notion of enforcement monitors is more amenable to implementation and encompasses previous runtime enforcement mechanisms.

Workshop website: http://www.spacios.eu/sectest2012/International audienceWe present an approach to detect web injection vulnerabilities by generating test inputs using a combination of model inference and evolutionary fuzzing. Model inference is used to obtain a knowledge about the application behavior. Based on this understanding, inputs are generated using genetic algorithm (GA). GA uses the learned formal model to automatically generate inputs with better fitness values towards triggering an instance of the given vulnerability