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LLMs used to craft persuasive attacks that fool fact-checking systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for attacking automated fact-checking (AFC) systems by leveraging large language models (LLMs) to rephrase claims using persuasive language. This approach, which categorizes $15$ persuasion techniques, was tested on the FEVER and FEVEROUS benchmarks. The experiments demonstrated that these persuasive attacks significantly impair both the accuracy of claim verification and the retrieval of supporting evidence, indicating a vulnerability in current AFC systems. AI

IMPACT Highlights a new vulnerability in AI fact-checking systems, necessitating more robust defenses against LLM-driven disinformation.

RANK_REASON The cluster contains an academic paper detailing a new research finding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs used to craft persuasive attacks that fool fact-checking systems

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The cluster contains an academic paper detailing a new research finding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jo\~ao A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, Carolina Scarton ·

    LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems

    arXiv:2601.16890v2 Announce Type: replace-cross Abstract: Automated fact-checking (AFC) systems are susceptible to adversarial attacks, enabling false claims to evade detection. Existing adversarial frameworks typically rely on injecting noise or altering semantics, yet no existi…

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