Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System
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.
689b2beffab6fdd2e52ac6aa
Haorui He
claimed_verified
2025-08-14T13:39:10.592Z
61a7569eaf0333e76eb428a8
https://cdn-avatars.huggingface.co/v1/production/uploads/61a7569eaf0333e76eb428a8/zwseNheR4Hx0DtCmf_v5H.jpeg
HarryHe11
user
HarryHe
689b2beffab6fdd2e52ac6ab
Yupeng Li
689b2beffab6fdd2e52ac6ac
Bin Benjamin Zhu
689b2beffab6fdd2e52ac6ad
Dacheng Wen
689b2beffab6fdd2e52ac6ae
Reynold Cheng
689b2beffab6fdd2e52ac6af
Francis C. M. Lau
Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System
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.
Fact2Fiction is a poisoning attack framework that targets agentic fact-checking systems by exploiting their decomposition strategy and justifications, achieving higher attack success rates than existing methods.
LLM-based agents sub-claims verification justifications poisoning attack framework Fact2Fiction attack success rates security weaknesses defensive countermeasures