International audienceAutomata learning techniques are getting significant importance for their applications in a wide variety of software engineering problems, especially in the analysis and testing of complex systems. In recent studies, a previous learning approach [1] has been extended to synthesize Mealy machine models which are specifically tailored for I/O based systems. In this paper, we discuss the inference of Mealy machines and propose improvements that reduces the worst-time learning complexity of the existing algorithm. The gain over the complexity of the proposed algorithm has also been confirmed by experimentation on a large set of finite state machines

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International audienceFuzzing is one of the most popular test-based software vulnerability detection techniques. It consists in running the target application with dedicated inputs in order to exhibit potential failures that could be exploited by a malicious user. In this paper we propose a global approach for fuzzing, addressing the main challenges to be faced in an industrial context: large-size applications, without source code access, and with a partial knowledge of the input specifications. This approach integrates several successive steps, and we mostly focus here on an important one which relies on binary-level dynamic taint analysis. We summarize the main problems to be addressed in this step, and we detail the solution we implemented to solve them

International audienceNowadays, one of the most effective ways to identify software vulnerabilities by testing is the use of fuzzing, whereby the robustness of software is tested against invalid inputs that play on implementation limits or data boundaries. A high number of random combinations of such inputs are sent to the system through its interfaces. Although fuzzing is a fast technique which detects real errors, its efficiency should be improved. Indeed, the main drawbacks of fuzz testing are its poor coverage which involves missing many errors, and the quality of tests. Enhancing fuzzing with advanced approaches such as: data tainting and coverage analysis would improve its efficiency and make it smarter. This paper will present an idea on how these techniques when combined give better error detection by iteratively guiding executions and generating the most pertinent test cases able to trigger potential vulnerabilities and maximize the coverage of testing

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