  Rug pulls pose a grave threat to the cryptocurrency ecosystem, leading to
substantial financial loss and undermining trust in decentralized finance
(DeFi) projects. With the emergence of new rug pull patterns, research on rug
pull is out of state. To fill this gap, we first conducted an extensive
analysis of the literature review, encompassing both scholarly and industry
sources. By examining existing academic articles and industrial discussions on
rug pull projects, we present a taxonomy inclusive of 34 root causes,
introducing six new categories inspired by industry sources: burn, hidden
owner, ownership transfer, unverified contract, external call, and fake LP
lock. Based on the developed taxonomy, we evaluated current rug pull datasets
and explored the effectiveness and limitations of existing detection
mechanisms. Our evaluation indicates that the existing datasets, which document
2,448 instances, address only 7 of the 34 root causes, amounting to a mere 20%
coverage. It indicates that existing open-source datasets need to be improved
to study rug pulls. In response, we have constructed a more comprehensive
dataset containing 2,360 instances, expanding the coverage to 54% with the best
effort. In addition, the examination of 14 detection tools showed that they can
identify 25 of the 34 root causes, achieving a coverage of 73.5%. There are
nine root causes (Fake LP Lock, Hidden Fee, and Destroy Token, Fake Money
Transfer, Ownership Transfer, Liquidity Pool Block, Freeze Account,
Wash-Trading, Hedge) that the existing tools cannot cover. Our work indicates
that there is a significant gap between current research and detection tools,
and the actual situation of rug pulls.
