AI RESEARCH

WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections

arXiv CS.AI

ArXi:2605.15030v1 Announce Type: cross Web agents can autonomously complete online tasks by interacting with websites, but their exposure to open web environments makes them vulnerable to prompt injection attacks embedded in HTML content or visual interfaces. Existing guard models still suffer from limited generalization to unseen domains and attack patterns, high false positive rates on benign content, reduced deployment efficiency due to added latency at each step, and vulnerability to adversarial attacks that evolve over time or directly target the guard itself.