@inproceedings{liu-etal-2026-defending,
title = "Defending Agentic Fact-Checking Against Adversarial Attacks",
author = "Liu, Fanzhen and
Xu, Tingyang and
Lin, Xixun and
Xiang, Haolong and
Zhang, Ge and
Du, Anan and
Pang, Shuchao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2158/",
pages = "49097--49122",
ISBN = "979-8-89176-459-0",
abstract = "Agentic fact-checking (FC) has emerged as a powerful paradigm for verifying complex claims through dynamic reasoning, tool use, and iterative evidence gathering. Understanding the vulnerability of these systems is essential for assessing their reliability against misinformation and adversarial manipulation. In this work, we study both the offensive and defensive aspects of agentic FC. First, we identify a limitation in the state-of-the-art Fact2Fiction attack, which introduces excessive semantic drift into malicious evidence, and propose F+, a relevance-filtering framework that generates more semantically consistent adversarial evidence. Second, we introduce SRSFact, an enhanced agentic FC system with a Supporter{--}Refuter{--}Summarizer mechanism for structured evidence authenticity assessment, improving robustness without relying on external open-web knowledge search. Experiments on the AVeriTeC benchmark show that F+ significantly strengthens attack effectiveness, while SRSFact substantially reduces failure rates under diverse adversarial attacks."
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%0 Conference Proceedings
%T Defending Agentic Fact-Checking Against Adversarial Attacks
%A Liu, Fanzhen
%A Xu, Tingyang
%A Lin, Xixun
%A Xiang, Haolong
%A Zhang, Ge
%A Du, Anan
%A Pang, Shuchao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-defending
%X Agentic fact-checking (FC) has emerged as a powerful paradigm for verifying complex claims through dynamic reasoning, tool use, and iterative evidence gathering. Understanding the vulnerability of these systems is essential for assessing their reliability against misinformation and adversarial manipulation. In this work, we study both the offensive and defensive aspects of agentic FC. First, we identify a limitation in the state-of-the-art Fact2Fiction attack, which introduces excessive semantic drift into malicious evidence, and propose F+, a relevance-filtering framework that generates more semantically consistent adversarial evidence. Second, we introduce SRSFact, an enhanced agentic FC system with a Supporter–Refuter–Summarizer mechanism for structured evidence authenticity assessment, improving robustness without relying on external open-web knowledge search. Experiments on the AVeriTeC benchmark show that F+ significantly strengthens attack effectiveness, while SRSFact substantially reduces failure rates under diverse adversarial attacks.
%U https://aclanthology.org/2026.emnlp-main.2158/
%P 49097-49122
Markdown (Informal)
[Defending Agentic Fact-Checking Against Adversarial Attacks](https://aclanthology.org/2026.emnlp-main.2158/) (Liu et al., EMNLP 2026)
ACL
- Fanzhen Liu, Tingyang Xu, Xixun Lin, Haolong Xiang, Ge Zhang, Anan Du, and Shuchao Pang. 2026. Defending Agentic Fact-Checking Against Adversarial Attacks. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49097–49122, Budapest, Hungary. Association for Computational Linguistics.