Deep learning-based software security detection systems act as the 'security inspectors' of the digital age, efficiently identifying software vulnerabilities. However, a study named EaTVul has unveiled new challenges in this field. EaTVul is an innovative evasion attack strategy that modifies vulnerable code, leading deep learning detection systems to misjudge, achieving success rates ranging from 83% to 100%. Its method includes using support vector machines to identify key samples, attention mechanisms to recognize critical features, AI chatbots to generate confusing data, and fuzzy logic.
