arXiv AI

Identifying AI Web Scrapers Using Canary Tokens

The paper introduces a method to detect which web scrapers feed data to large language models (LLMs) by deploying dynamic websites that issue unique canary tokens to each scraper. By querying LLMs for information about these sites, the authors can identify when an LLM consistently outputs the unique tokens, indicating exposure to a specific scraper. Experiments on 22 production LLM systems show the technique reliably uncovers both known and undisclosed scrapers, offering a tool for third parties to monitor and control unwanted web scraping.

arXiv AI
Oct 28

DeGenTWeb: A First Look at MGT-dominant Websites

DeGenTWeb is a system that identifies websites whose informational prose is largely machine‑generated text (MGT) with minimal human input. By adapting MGT detectors for web pages and aggregating results across sites, the study reports that 20.9% of Common Crawl‑archived sites (Jan–Jul 2026) and 13.6% of Bing‑linked sites (2025) are MGT‑dominant, with a 0.12% mislabeling rate for human‑authored sites. The prevalence of MGT‑dominant sites is increasing over time and many share the same owner.

By Sichang Steven He, Calvin Ardi, Ramesh Govindan, Harsha V. Madhyastha
arXiv Computation and Language
2d ago

When 200 OK Is Not an Answer: Silent Fetch Failures in Agentic Web Content Acquisition

The study investigates how often web agents incorrectly treat an HTTP 200 response as a successful fetch of useful content. By sampling 10,307 URLs—including random paths and expert‑verified claim citations—the authors found that roughly 20–25% of 200 responses lack the page’s main content, leading to inflated estimates of claim verifiability and citation drift. They propose simple heuristics and classifiers to detect unusable responses, achieving high recall with low false‑alarm rates, and recommend that agents report fetch outcomes explicitly.

By Shuyang Zhang (The Hong Kong Polytechnic University)
arXiv AI
Sep 10

Evaluating Deep-Search Agents under Hierarchical Web Evidence Poisoning

arXiv:2609.06027v1 Announce Type: cross Abstract: Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. E...

By Zhongan Bi, Qiwen Wang, Jianrong Jiang, Jigang Ding, Wenwen Xiong, Changhua Meng, Xuanang Gao, Kepeng Lin, Changjiang Jiang, Yiang Chen, Huan Yao, Wei Wang, Zhenyu Ma, Wenhui Dong
arXiv Computation and Language
Sep 1

FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents

arXiv:2510.03204v2 Announce Type: replace Abstract: Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens...

By Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar, Xing Han L\`u, L\'eo Boisvert, Massimo Caccia, J\'er\'emy Espinas, Alexandre Aussem, V\'eronique Eglin, Alexandre Lacoste
arXiv AI
Jun 16

MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

arXiv:2602. 09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf.

By Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea