ADVERSIAL DEEP NEURAL NETWORK FUZZING

A method for detecting security vulnerabilities, comprising: generating a corpus of input samples each labeled to indicate a threat level when executed by an input processing code; training a neural network (NN) using the plurality of input samples to classify inputs according to a plurality of labels of the plurality of input samples; for each input sample: iteratively altering the input sample to correspond to a process of gradient change of the NN, until the NN classifies the altered input sample to a different label than a respective label of the input sample; assigning the different label to the altered input sample; using the plurality of relabeled altered input samples to further train the NN and augment the corpus of input samples.