🤖 AI Summary
This work addresses the critical need for accurate and efficient detection of security vulnerabilities in Ethereum smart contracts, which can lead to severe financial losses. We propose VASCOT, the first Transformer-based framework for vulnerability detection at the EVM bytecode level. By serializing bytecode and incorporating a sliding window mechanism, VASCOT effectively handles long input sequences, overcoming the length limitations of conventional models. A high-quality dataset, annotated based on execution traces, was constructed to support this approach. Evaluated on 16,469 real-world contracts deployed in 2022, VASCOT significantly outperforms LSTM-based baselines in detection performance and demonstrates superior generalization capability, establishing a new paradigm for smart contract security analysis.