Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System
Authors: name: Haorui He, name: Yupeng Li, name: Bin Benjamin Zhu, name: Dacheng Wen, name: Reynold Cheng, name: Francis C. M. Lau

State-of-the-art fact-checking systems combat misinformation at scale 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 (explanatory rationales for the
verdicts). The security of these systems is crucial, as compromised
fact-checkers, which tend to be easily underexplored, can amplify
misinformation. This work introduces Fact2Fiction, the first poisoning attack
framework targeting such agentic fact-checking systems. Fact2Fiction mirrors
the decomposition strategy and exploits 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 state-of-the-art attacks across various
poisoning budgets. Fact2Fiction exposes security weaknesses in current
fact-checking systems and highlights the need for defensive countermeasures.