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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.CL English(EN) · · [2 sources]

    FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection

    Researchers have introduced FraudSMSWalker, a new benchmark designed to evaluate the capabilities of agentic large language models in detecting SMS-based fraud that directs users to malicious webpages. The benchmark masks URLs and other reputation shortcuts, forcing models to rely solely on the SMS content and sanitized webpage evidence to make fraud judgments. Initial evaluations show that while current agents can identify some suspicious cues, they struggle with maintaining accuracy for benign cases and often base their predictions on weak evidence. AI

    IMPACT This benchmark aims to improve LLM agents' ability to detect sophisticated cross-channel fraud by removing reputation shortcuts.

  2. TOOL · arXiv cs.CL English(EN) ·

    WCXB: A Multi-Type Web Content Extraction Benchmark

    Researchers have introduced the Web Content Extraction Benchmark (WCXB), a new dataset designed to improve the evaluation of systems that isolate main content from web pages. The WCXB dataset comprises 2,008 web pages from 1,613 domains, covering seven distinct page types beyond just news articles. Evaluations on this benchmark revealed significant performance disparities among extraction systems, particularly on structured page types, highlighting limitations of existing article-centric benchmarks. AI

    WCXB: A Multi-Type Web Content Extraction Benchmark

    IMPACT Provides a more comprehensive evaluation for web content extraction systems, crucial for LLM training and RAG.

  3. TOOL · r/OpenAI English(EN) ·

    What actually happens when a webpage hijacks your AI agent

    A security researcher demonstrated how easily AI agents can be tricked into executing malicious instructions embedded within webpages. By including hidden commands in a webpage's footer, the agent can be prompted to ignore its original directives and send sensitive information, such as API credentials, to an attacker. While explicit "ignore previous instructions" commands are detectable, subtler, implicitly worded instructions are proving to be a more significant and unsolved security challenge for current AI agent architectures. AI

    IMPACT Highlights a critical security flaw in current AI agent designs, necessitating the development of robust governance layers to prevent data exfiltration and unauthorized actions.