  Mis- and disinformation are a substantial global threat to our security and
safety. To cope with the scale of online misinformation, researchers have been
working on automating fact-checking by retrieving and verifying against
relevant evidence. However, despite many advances, a comprehensive evaluation
of the possible attack vectors against such systems is still lacking.
Particularly, the automated fact-verification process might be vulnerable to
the exact disinformation campaigns it is trying to combat. In this work, we
assume an adversary that automatically tampers with the online evidence in
order to disrupt the fact-checking model via camouflaging the relevant evidence
or planting a misleading one. We first propose an exploratory taxonomy that
spans these two targets and the different threat model dimensions. Guided by
this, we design and propose several potential attack methods. We show that it
is possible to subtly modify claim-salient snippets in the evidence and
generate diverse and claim-aligned evidence. Thus, we highly degrade the
fact-checking performance under many different permutations of the taxonomy's
dimensions. The attacks are also robust against post-hoc modifications of the
claim. Our analysis further hints at potential limitations in models' inference
when faced with contradicting evidence. We emphasize that these attacks can
have harmful implications on the inspectable and human-in-the-loop usage
scenarios of such models, and we conclude by discussing challenges and
directions for future defenses.
