Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with...
RiskChainBench is a new benchmark that pairs 3,600 synthetic token‑text restoration inputs with 600 human‑labeled local web environments to evaluate how well models can recover obfuscated platform messages and then investigate the associated websites. The benchmark measures both message restoration accuracy and the subsequent web‑investigation decision, using a fixed multimodal evidence judge to assess faithfulness, sufficiency, completeness, and consistency. Across ten models, performance varies widely, with entry recovery ranging from 35.2% to 95.2% and web decision accuracy from 26.3% to 62.8%, highlighting execution failures as the main bottleneck.
By ZhuoXin Liu, Zhiming Ma, Ying Zhang, Mengzheng Yang, Yifan Wang, Zhengqi Huang, Yanhan Zhou, Zekun Lin, Jun Zhang, Shun Zhang, Yue Chen, Qiao Zhao, Peng Chen
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.
By Steven Seiden, Triss Ren, Caroline Zhang, Taein Kim, Enze Liu, Emily Wenger
TriShieldRAG introduces a three‑layered defense for Retrieval‑Augmented Generation: an Ingest Guard that screens documents, a Retrieval Scorer that re‑ranks based on trust, and a Cross‑LLM Consensus that validates evidence across three models. Against the original PoisonedRAG attack on the 2.68M‑passage Natural Questions corpus, the framework reduces attack success from about 79% to 1%. However, adaptive attacks that only alter document formatting can bypass the Ingest Guard and still achieve high success rates, revealing limits of layered defenses that rely on the same retrieved evidence.
By Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary, Disha Singhania
arXiv:2607.10198v2 Announce Type: replace
Abstract: Search APIs expose ranked snippets, URLs, and metadata on which agents decide whether to answer, search again, or fetch pages. We evaluate these in...
By Sriram Selvam, Anneswa Ghosh
AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos