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Increasing numbers of software vulnerabilities are discovered every yearwhether they are reported publicly or discovered internally in proprietarycode. These vulnerabilities can pose serious risk of exploit and result insystem compromise, information leaks, or denial of service. We leveraged thewealth of C and C++ open-source code available to develop a large-scalefunction-level vulnerability detection system using machine learning. Tosupplement existing labeled vulnerability datasets, we compiled a vast datasetof millions of open-source functions and labeled it with carefully-selectedfindings from three different static analyzers that indicate potentialexploits. The labeled dataset is available at: https://osf.io/d45bw/. Usingthese datasets, we developed a fast and scalable vulnerability detection toolbased on deep feature representation learning that directly interprets lexedsource code. We evaluated our tool on code from both real software packages andthe NIST SATE IV benchmark dataset. Our results demonstrate that deep featurerepresentation learning on source code is a promising approach for automatedsoftware vulnerability detection.

Automated Vulnerability Detection in Source Code Using Deep Representation Learning