SoK: Comprehensive Analysis of Rug Pull Causes, Datasets, and Detection Tools in DeFi (2403.16082v1)

Abstract: 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.

• The paper introduces the Rug Pull Analysis Framework (RPAF) that categorizes 34 rug pull causes and quantifies associated financial losses.
• It enhances dataset coverage from 20% to 54% by augmenting existing datasets with 2,360 rug pull instances.
• It assesses 14 detection tools, revealing a 73.5% coverage while highlighting significant detection gaps for critical root causes.

Comprehensive Analysis of Rug Pulls in DeFi

This paper presents a systematic and comprehensive analysis of rug pulls in the DeFi ecosystem, addressing the gaps in current research by providing an in-depth taxonomy of rug pull causes, evaluating existing datasets, and assessing the capabilities of current detection tools. The research introduces the Rug Pull Analysis Framework (RPAF), which encompasses a thorough literature review, taxonomy creation, dataset critique and reconstruction, and assessment of detection tools.

Research Methodology

The RPAF methodology consists of two primary stages: systematic literature review and analysis design (Figure 1). The systematic literature review involves the collection of academic research papers and industry reports on rug pulls. The analysis process extracts a preliminary rug pull taxonomy from the collected materials and expands it based on new rug pull cases. The developed taxonomy is then used to evaluate current rug pull datasets and augment them with new rug pull patterns. Finally, detection tools are evaluated based on the reconstructed dataset and the developed taxonomy.

Figure 1: The Rug Pull Analysis Framework (RPAF) consists of systematic literature review and analysis design to create a taxonomy, evaluate datasets, and assess detection tools.

Taxonomy of Rug Pull Root Causes

The paper develops a comprehensive taxonomy of rug pulls, encompassing 34 root causes categorized into 6 high-level categories and 19 root cause categories (Table 1). This taxonomy expands upon existing frameworks by incorporating new categories inspired by industry sources, such as burn, hidden owner, ownership transfer, unverified contract, external call, and fake LP lock. The taxonomy also quantifies the financial losses associated with each type of rug pull, revealing a total financial impact of $$171,713,065, with token distribution accounting for the most significant economic loss (35<h2 class='paper-heading' id='evaluation-of-existing-datasets'>Evaluation of Existing Datasets</h2> <p>The research evaluates the coverage of current rug pull datasets, finding that existing datasets address only 7 of the 34 root causes, amounting to a mere 20<h2 class='paper-heading' id='assessment-of-detection-tools'>Assessment of Detection Tools</h2> <p>The capabilities and limitations of 14 current rug pull detection tools are systematically evaluated. The evaluation reveals that these tools can identify 25 of the 34 root causes, achieving a coverage of 73.5<h2 class='paper-heading' id='results-and-insights'>Results and Insights</h2> <p><img src="https://emergentmind-storage-cdn-c7atfsgud9cecchk.z01.azurefd.net/paper-images/2403-16082/motivation_example_code2.21.png" alt="Figure 2" title="" class="markdown-image"> <p class="figure-caption">Figure 2: The code backdoor for$$DOGE3.0 illustrates the project's hidden owner address and burning function.

The research findings highlight the evolving complexity of rug pull techniques, with scammers shifting from simple methods to more deceptive, hybrid approaches. The case of Dogecoin 3.0 ($DOGE3.0) illustrates the limitations of current taxonomies and detection tools, as the rug pull method employed in this case is not encapsulated by any existing taxonomy nor detectable by current open-source or commercial tools. The code backdoor implemented for the rug pull (Figure 2) demonstrates the use of proxy contracts to hide the true owner, incorporating hidden minting functions in the contract, and manipulating the total supply to create tokens out of thin air.

Threats to Validity

The paper acknowledges the potential for bias within the manual classification and annotation process. The dynamic nature of the blockchain and cryptocurrency landscape also poses a threat, as novel rug pull strategies could emerge that challenge the current taxonomy's pertinence to future conditions.

Conclusion

This paper provides a comprehensive analysis of rug pulls in the DeFi ecosystem, offering a detailed taxonomy of root causes, evaluating existing datasets, and assessing the capabilities of current detection tools. The research highlights the need for continuous improvement in detection capabilities to address the evolving complexity of rug pull techniques. The augmented dataset and the developed taxonomy contribute to a better understanding of rug pulls and can benefit the research community in developing more effective detection and prevention strategies.