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.

翻译：Rug Pull 对加密货币生态系统构成严重威胁，导致巨大财务损失并削弱对去中心化金融（DeFi）项目的信任。随着新型 Rug Pull 模式的出现，现有研究已无法反映其当前状态。为填补这一空白，我们首先对文献进行了广泛分析，涵盖学术与行业来源。通过考察现有学术论文及业界关于 Rug Pull 项目的讨论，我们提出了一份包含34个根本原因的分类体系，并引入六类源自行业来源的新类别：销毁、隐藏所有者、所有权转移、未验证合约、外部调用及虚假流动性锁定。基于该分类体系，我们评估了当前 Rug Pull 数据集，并探究了现有检测机制的有效性与局限性。评估表明，现有数据集记录2,448个实例，仅覆盖34个根本原因中的7个，覆盖率仅20%。这说明研究 Rug Pull 需改进现有开源数据集。为此，我们构建了更全面的数据集，包含2,360个实例，在最大努力下将覆盖率提升至54%。此外，对14种检测工具的分析显示，它们能识别34个根本原因中的25个，达到73.5%的覆盖率。仍有九个根本原因（虚假流动性锁定、隐藏费用、销毁代币、虚假转账、所有权转移、流动性池冻结、账户冻结、洗售交易、对冲）无法被现有工具覆盖。研究表明，当前研究与检测工具与 Rug Pull 实际情况之间存在显著差距。