Work

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

Year: 2026

Type: article

Abstract: 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 its input-output mapping. 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.

Source: Journal of Systems and Software

DOI: 10.1016/j.jss.2026.112856

Authors: Samuele Pasini, Gianluca Maragliano, Jinhan Kim, Paolo Tonella

Institution: Università della Svizzera italiana

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,820

Funders: European Commission, HORIZON EUROPE Framework Programme