Fact2Fiction：针对智能体事实核查系统的定向投毒攻击
Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System
AI总结：
针对当前基于LLM智能体的SOTA事实核查系统存在的安全研究空白，提出首个定向投毒攻击框架Fact2Fiction，实验证明其攻击成功率显著优于现有SOTA攻击，暴露了现有系统的安全缺陷。
摘要：
当前最先进（SOTA）的事实核查系统通过基于大语言模型（LLM）的自主智能体对抗虚假信息，这类系统会将复杂声明拆解为更小的子声明，逐一验证每个子声明，再聚合部分结果生成附带判定依据（即判定结果的解释）的结论。这类系统的安全性至关重要，因为被攻陷的事实核查工具会放大虚假信息，但目前其安全性仍存在大量研究空白。为填补这一空白，本研究提出了针对此类事实核查系统的新型威胁模型，并推出了Fact2Fiction，这是首个面向SOTA智能体事实核查系统的投毒攻击框架。Fact2Fiction利用LLM模拟声明拆解策略，利用系统生成的判定依据制作定制化恶意证据，从而破坏子声明验证环节。大量实验表明，在不同投毒预算设置下，Fact2Fiction的攻击成功率比现有SOTA攻击高出8.9%至21.2%，暴露了现有事实核查系统的安全弱点，凸显了部署防御对策的必要性。
英文摘要：
State-of-the-art (SOTA) fact-checking systems combat misinformation by employing autonomous LLM-based agents to decompose complex claims into smaller sub-claims, verify each sub-claim individually, and aggregate the partial results to produce verdicts with justifications (explanations for the verdicts). The security of these systems is crucial, as compromised fact-checkers can amplify misinformation, but remains largely underexplored. To bridge this gap, this work introduces a novel threat model against such fact-checking systems and presents \textsc{Fact2Fiction}, the first poisoning attack framework targeting SOTA agentic fact-checking systems. Fact2Fiction employs LLMs to mimic the decomposition strategy and exploit system-generated justifications to craft tailored malicious evidences that compromise sub-claim verification. Extensive experiments demonstrate that Fact2Fiction achieves 8.9\%--21.2\% higher attack success rates than SOTA attacks across various poisoning budgets and exposes security weaknesses in existing fact-checking systems, highlighting the need for defensive countermeasures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
