  Automated fact-checking is a needed technology to curtail the spread of
online misinformation. One current framework for such solutions proposes to
verify claims by retrieving supporting or refuting evidence from related
textual sources. However, the realistic use cases for fact-checkers will
require verifying claims against evidence sources that could be affected by the
same misinformation. Furthermore, the development of modern NLP tools that can
produce coherent, fabricated content would allow malicious actors to
systematically generate adversarial disinformation for fact-checkers.
  In this work, we explore the sensitivity of automated fact-checkers to
synthetic adversarial evidence in two simulated settings: AdversarialAddition,
where we fabricate documents and add them to the evidence repository available
to the fact-checking system, and AdversarialModification, where existing
evidence source documents in the repository are automatically altered. Our
study across multiple models on three benchmarks demonstrates that these
systems suffer significant performance drops against these attacks. Finally, we
discuss the growing threat of modern NLG systems as generators of
disinformation in the context of the challenges they pose to automated
fact-checkers.
