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Cross-site scripting adversarial attacks based on deep reinforcement learning: Evaluation and extension study

Samuele Pasini et al.

Journal of Systems and Software2026https://doi.org/10.1016/j.jss.2026.112856article
AJG 2
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1016/j.jss.2026.112856

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@article{samuele2026,
  title        = {{Cross-site scripting adversarial attacks based on deep reinforcement learning: Evaluation and extension study}},
  author       = {Samuele Pasini et al.},
  journal      = {Journal of Systems and Software},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jss.2026.112856},
}

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Cross-site scripting adversarial attacks based on deep reinforcement learning: Evaluation and extension study

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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