AI-Based Solution Lightning Cat Aims to Enhance Smart Contract Security

According to Decrypt, a team of researchers has proposed an artificial intelligence-based solution called Lightning Cat to identify vulnerabilities in smart contracts. The solution uses deep learning techniques and is based on three optimized deep learning models: CodeBERT, LSTM, and CNN. These models are trained on data sets comprising thousands of vulnerable contracts. The CodeBERT model outperforms static detection tools, demonstrating an impressive f1-score of 93.53%, accurately capturing the syntax and semantics of the code and proving itself a capable blockchain auditor. However, Lightning Cat comes with some risks, as it can be a double-edged sword. While it can enhance smart contract security, there's potential for malicious actors to exploit this technology, using it to detect bugs and exploit them instead of fixing them. To mitigate this, the researchers encourage coders to consider proper security practices and check their products regularly. They also recommend developers to regularly conduct code audits, undergo secure coding training, and adopt responsible vulnerability disclosure policies. The Lightning Cat initiative is part of a broader trend where AI and blockchain technologies are converging to enhance software security. This trend includes an AI and blockchain-based decentralized software testing system that combines the power of deep learning with the transparency and reliability of blockchain technology. This approach significantly accelerates the vulnerability detection process and is proving especially beneficial in remote work scenarios. Additionally, it incorporates the InterPlanetary File System (IPFS) for efficient data storage, offering a comprehensive solution for secure code development and testing in decentralized environments.