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AdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model

Paper recorded by Signals 4 on 2026-10-06 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.AI · 人工智能 · first seen 2026-10-07

Abstract

Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent away from the user's goal. The agent cannot simply ignore the page, because the page also holds the values and controls the task requires. Current defenses fine-tune the agent on injections fixed before training, and attackers that adapt to the train

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#406 most recent of 960 cs.AI papers we have recorded · ↑ newer: Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, · ↓ older: VeriFine: Scaling Verification for Self-Improvement in Embodied Reason
Cite this page: AdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model: the #406 most recent of 960 cs.AI papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/advsim2real-training-web-agents-against-adaptive-prompt-injection-in-a-web-world.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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