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Böttinger, Konstantin; Godefroid, Patrice; Singh, Rishabh (2018)
Languages: English
Types: Preprint
Subjects: Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security

Classified by OpenAIRE into

ACM Ref: TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS
Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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