Towards Attention Based Vulnerability Discovery Using Source Code Representation

Vulnerability discovery in software is an important task in the field of computer security. As vulnerabilities can be abused to enable cyber criminals and other malicious actors to exploit systems, it is crucial to keep software as free from vulnerabilities as is possible. Traditional approaches often comprise code scanning tasks to find specific and already-known classes of cyber vulnerabilities. However these approaches do not in general discover new classes of vulnerabilities. In this paper, we leverage a machine learning approach to model source code representation using syntax, semantics and control flow of source code and to infer vulnerable code patterns to tackle large code bases and identify potential vulnerabilities that missed by any existing static software analysis tools. In addition, our attention-based bidirectional long short-term memory framework adaptively localise regions of code illustrating where the possible vulnerable code fragment exists. The highlighted region may provide informative guidance to human developers or security experts. The experimental results demonstrate the feasibility of the proposed approach in the problem of software vulnerability discovery.