InfoResearchPreprintLLM-specific
Data-Void Exploits in Web-Searching LLMs
- Published
- Record updated
Summary
Researchers study data-void exploits, where disinformers publish narratives in search spaces with little reliable content so that web-searching LLMs retrieve and cite them. They test ChatGPT, Gemini and Sonar Pro across skeptical, excited and neutral prompt tones and three attacker controls. Attacker content appears in LLM responses 31% of the time, and a single attacker-controlled page can suffice, while prompt tone and model choice show no large effect on success rates.
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