Adaptive Detection of Unknown Threats in Smart Contracts via Multimodal Self-Learning

Existing AI systems for finding bugs in smart contracts (programs that run on blockchains) struggle because they only learn from known vulnerabilities and use limited types of information about the code. This paper presents Synesthete, a new detection method that combines multiple types of code features (text, graph structures, and images from different code representations) and uses self-learning to better identify both known and previously unseen vulnerabilities in smart contracts.