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arXiv AI By Jo\~ao A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, Carolina Scarton

LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems

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The paper introduces a new type of adversarial attack on automated fact‑checking systems that uses large language models to rephrase claims with persuasive techniques. By applying 15 persuasion methods across five categories, the authors evaluate how these rewrites affect claim verification and evidence retrieval on the FEVER and FEVEROUS benchmarks. Results show that persuasive rewrites significantly degrade both verification accuracy and evidence retrieval performance, underscoring the vulnerability of current fact‑checking systems to such attacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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