Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulnerable to adversarial attacks due to the discontinuous nature of the mapping between the input (i.e., the attack) and the output (i.e., the prediction of the model whether an input is classified as XSS or benign). These adversarial attacks employ mutation-based strategies for different components of XSS attack vectors, allowing adversarial agents to iteratively select mutations to evade detection. Our work replicates a state-of-the-art XSS adversarial attack, highlighting threats to validity in the reference work and extending it towards a more effective evaluation strategy. Moreover, we introduce an XSS Oracle to mitigate these threats. The experimental results show that our approach achieves an escape rate above 96% when the threats to validity of the replicated technique are addressed.

翻译：跨站脚本攻击对Web应用安全构成重大威胁。尽管深度学习在检测XSS攻击方面取得了显著成功，但由于输入（即攻击）与输出（即模型预测输入被分类为XSS或良性）之间映射的非连续性，其仍易受对抗攻击。这些对抗攻击对XSS攻击向量的不同组件采用基于变异的策略，使对抗代理能够迭代选择变异以逃避检测。我们的工作复现了一种最先进的XSS对抗攻击，揭示了原始研究中的效度威胁，并将其扩展为更有效的评估策略。此外，我们引入了一种XSS预言机以缓解这些威胁。实验结果表明，当复现技术的效度威胁得到解决时，我们的方法实现了超过96%的逃逸率。