The Corroboration Illusion: When More News Makes LLM Forecasts Less True
Large language models (LLMs) are increasingly used to forecast real-world events by retrieving and reasoning over news. We show that this dependence on an open, crawlable news corpus creates a new attack surface: an adversary who can merely publish articles--without access to the retriever, the model, or the user's queries--can systematically move the forecaster's output probabilities. We formalize news-corpus poisoning of probabilistic forecasters, a threat model distinct from prior RAG poisoning, which targets factual answers or opinion polarity rather than calibrated probabilities. We evaluate the attack on 500 resolved ForecastBench questions against a 17.4M-article Common Crawl News ...
| Authors | Yuan Lu, Yukuan Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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