Learning to map source code to software vulnerability using code-as-a-graph

Understand

We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective.

• Specifically, whether signatures of vulnerabilities in source code can be learned from its graph representation, in terms of relationships between nodes and edges.
• We create a pipeline we call AI4VA, which first encodes a sample source code into a Code Property Graph.
• The extracted graph is then vectorized in a manner which preserves its semantic information.