There have been recent adversarial attacks that are difficult to find. These new adversarial attacks methods may pose challenges to current deep learning cyber defense systems and could influence the future defense of cyberattacks. The authors focus on this domain in this research paper. They explore the consequences of vulnerabilities in AI systems. This includes discussing how they might arise, differences between randomized and adversarial examples and also potential ethical implications of vulnerabilities. Moreover, it is important to train the AI systems appropriately when they are in testing phase and getting them ready for broader use.

翻译：近期出现了一些难以察觉的对抗性攻击。这些新型对抗性攻击方法可能对当前的深度学习网络防御系统构成挑战，并可能影响未来网络攻击的防御策略。本文作者聚焦于该领域展开研究，探讨人工智能系统脆弱性所引发的后果，包括分析脆弱性产生的原因、随机样本与对抗性样本之间的差异，以及脆弱性可能带来的伦理影响。此外，在人工智能系统的测试阶段对其进行恰当训练，并使其为更广泛的应用做好准备，也具有重要意义。