Transaction-Related Rug Pull Analysis

Updated 6 August 2025

Transaction-related rug pulls are fraudulent schemes where project insiders exploit normal transaction flows—like liquidity withdrawals, mass token dumps, and project abandonment—to defraud investors.
Empirical studies reveal that over 80% of short-lived tokens and rapid liquidity drains on Ethereum, BNB Smart Chain, and Solana are linked to these scams, eroding trust in DeFi markets.
Detection methods employ time series, graph-based, and machine learning analyses to monitor abrupt liquidity shifts and identify coordinated scam patterns in real time.

A transaction-related rug pull is a fraudulent scheme within the cryptocurrency and decentralized finance (DeFi) ecosystem wherein malicious actors exploit ordinary transaction mechanisms—such as liquidity withdrawals, mass token dumps, or project abandonment—to extract user funds and leave investors with worthless tokens. Unlike contract-related rug pulls, these attacks do not require malicious code inserted into smart contracts but rely on control over token supply, liquidity, and privilege within the DeFi infrastructure. Transaction-related rug pulls are a pervasive problem across multiple blockchains, notably Ethereum, BNB Smart Chain, and Solana, causing substantial economic loss and eroding market trust.

1. Defining Characteristics and Taxonomy

Transaction-related rug pulls occur when project developers leverage their privileged access to perform illicit actions purely via on-chain transactions, without depending on hidden backdoors or malicious smart contract logic (Lin et al., 3 Mar 2024). The archetypal forms include:

Liquidity Withdrawal: The developer removes nearly all liquidity from a pool, leaving no market for remaining token holders.
Mass Token Dump (“Dumping Cryptocurrency”): The developer or insiders rapidly sell a large allocation of their tokens, crashing the market price.
Project Abandonment: After collecting user funds (via presale, initial liquidity, or token sales), the developer ceases all activity, deletes project communication channels, and leaves investors stranded.

Recent work (Sun et al., 24 Mar 2024) provides a standardized taxonomy of rug pull causes, identifying “Simple Rug Pull” and various “LP Manipulation” strategies as primary transaction-related categories. Notably, the taxonomy distinguishes between transaction-related rug pulls and smart-contract-based “trap doors,” emphasizing that the former do not require observable code-level vulnerabilities.

2. Prevalence, Patterns, and Structural Dynamics

Large-scale studies show that transaction-related rug pulls dominate both the Ethereum and BNB Smart Chain ecosystems, as well as emerging DeFi platforms like Solana (Cernera et al., 2022, Alhaidari et al., 6 Apr 2025). Empirical observations demonstrate:

Short-lived tokens: Approximately 60% of newly created tokens are active for less than one day (“1-day” tokens), with more than 80% of these associated with liquidity pools that are rugged shortly after launch (Cernera et al., 2022, Huynh et al., 14 Dec 2024).
Disposable scam patterns: A small set of “token spammers” (1% of addresses) are responsible for generating a disproportionate number of these short-lived tokens, orchestrating hundreds of thousands of rug pulls in serial or clustered fashion (Huynh et al., 14 Dec 2024). These groups deploy distinctive “star,” “chain,” and “majority-flow cluster” transaction patterns, channeling funds among coordinated scammer addresses.

Within each scam cluster, the source code of scam token contracts exhibits high intra-cluster similarity (typically Jaccard similarity > 0.7), confirming that individual scam organizations repeatedly deploy near-identical contracts to facilitate these transaction-driven rug pulls (Huynh et al., 14 Dec 2024).

3. Detection Methodologies and Analytical Models

Detecting transaction-related rug pulls presents unique challenges due to their reliance on normal, albeit malicious, transaction flows rather than explicit code vulnerabilities (Lin et al., 3 Mar 2024). The main approaches combine behavioral, statistical, and graph-based analyses:

Time Series and Transaction Behavior Analysis: Methods extract features such as abrupt liquidity withdrawal, sharp price drops (drawdown), and lack of subsequent recovery. The maximum drawdown metric, $\mathrm{MD} = |(X_l - X_h) / X_h|$ , and recovery metric, $\mathrm{RC} = (X_S - X_l)/(X_h - X_l)$ , quantify the severity and irreversibility of liquidity or price declines (Mazorra et al., 2022, Huang et al., 2023).
Propagation and Concentration Metrics: The Herfindahl–Hirschman Index (HHI) is computed over account balances to assess distribution concentration. Highly concentrated or rapidly shifting HHI may presage a rug pull (Mazorra et al., 2022). Clustering coefficients on the transaction graph further characterize token holder interaction patterns.
Machine Learning and Rule-based Detectors: State-of-the-art models leverage boosted trees (e.g., XGBoost), transformers for tabular data, and even ensemble approaches to flag tokens as potential rug pulls, achieving high recall and precision prior to the malicious event (Mazorra et al., 2022, Huang et al., 2023). Rule-based systems combine profit analysis, social media checks, and on-chain activity drop-offs to provide post-hoc and early warning signals (Huang et al., 2023).
Graph-based and Network-Aware Analysis: Cluster-aware profit models correct for overestimation present in prior work by including “wash trading” costs in their profit calculation. The corrected profit equation is $\delta(p, N) = Y(p, N) - X(p, N)$ with $X(p, N)$ incorporating both direct scammer funding and coordinated wash trading volumes (Huynh et al., 14 Dec 2024).

Dataset-driven approaches, such as those using SolRPDS for Solana (Alhaidari et al., 6 Apr 2025), use features like add-to-remove liquidity ratios and inactivity intervals to train ensemble models (e.g., Random Forest, AdaBoost) with >97% accuracy in distinguishing active from rugged tokens.

4. Economic Impact and Behavioral Insights

Transaction-related rug pulls have resulted in substantial economic damage:

Losses per event frequently exceed hundreds of thousands or millions of USD, with aggregate losses surpassing hundreds of millions on single blockchains (Cernera et al., 2022, Lin et al., 3 Mar 2024).
The typical rug pull pool generates ~$1,477 profit on Uniswap, but exceptional cases can exceed$1.5 million (Xia et al., 2021).
A high proportion of investors are lured in via initial trading activity, but >86% of scam pools have all liquidity drained within one day (and 37% within an hour), leaving tokens unsellable (Xia et al., 2021).
The attacker’s operational costs are low—0.03 BNB ($3–$10) or 0.2 ETH ($300–$600) per scam—making repeated attacks profitable even with moderate success rates (Cernera et al., 2022).

Additionally, the prevalence of “collusion” addresses and sniper bots further amplifies these effects. Collusion addresses are orchestrated to provide false liquidity signals, execute swaps that attract real users, and rapidly transfer profits back to the scam operator (Xia et al., 2021, Huynh et al., 14 Dec 2024). Sniper bots, acting with sub-5 block latency, facilitate both the rapid exploitation of new pools and, paradoxically, become frequent victims by buying into scam tokens that are immediately rugged (Cernera et al., 2022).

5. Defensive Mechanisms, Mitigation Protocols, and Recommendations

Given the non-programmed nature of transaction-related rug pulls, detection and prevention require continuous behavioral monitoring and economic incentive realignment rather than sole reliance on static code audits. Recommended measures include:

Real-time Monitoring and Early Warning Systems: On-chain analytics platforms and machine-learning-based detectors should be integrated into DEX frontends, wallet software, and aggregator services to flag tokens or pools exhibiting abnormal liquidity, price, or propagation dynamics (Mazorra et al., 2022, Huang et al., 2023, Alhaidari et al., 6 Apr 2025).
Reputation and Listing Policies: DEXes may adopt optional community or third-party verification layers and reputation scores to highlight high-risk or unverified pools/tokens without undermining decentralization (Xia et al., 2021).
Liquidity Lock Mechanisms: Where feasible, projects and DeFi protocols are urged to establish enforceable liquidity time locks or publish proof of locked LP tokens. However, analysis has shown that reliance on liquidity locking alone is insufficient, as up to 90% of tokens with “locked” liquidity eventually engage in rug pulls (Mazorra et al., 2022).
Economic Recovery Protocols: Protocols such as Rugsafe propose multichain vaults where “rugged” tokens can be deposited and transformed into inverse-pegged anticoins, which serve as synthetic recovery assets and convey dynamic rewards. The supply of the native protocol token is adjusted based on the volume and value of rugged tokens deposited, ensuring economic incentive alignment and providing post-facto recovery paths (PHarr et al., 8 Jul 2025).
On-chain and Off-chain Data Fusion: Hybrid approaches, such as the Kosmosis knowledge graph, fuse on-chain transaction data and off-chain (social media, enrichment) data to construct high-dimensional, semantically-annotated graphs. This enhances the capacity to issue real-time risk alerts and supports cross-entity forensic analytics (Stangl et al., 30 May 2024).
Dataset and Tool Enhancements: Current detection tools, while able to catch 73.5% of rug pull root causes, still miss critical categories including “Fake LP Lock,” “Hidden Fee,” and “Ownership Transfer.” Continued expansion of datasets and adaptive detection frameworks is necessary to close this detection gap (Sun et al., 24 Mar 2024).

6. Broader Implications and Future Directions

The persistence and adaptability of transaction-related rug pulls present acute challenges for DeFi ecosystem integrity:

Sole reliance on smart contract analysis or code audits leaves investors exposed to behavioral risks; dynamic, cross-layer monitoring and analytic fusion are increasingly mandatory.
The rapid turnover—e.g., 75% of inactive Solana tokens have a lifespan under one day—underscores the imperative of near-real-time, automated detection methodologies (Alhaidari et al., 6 Apr 2025).
Economic recovery protocols and incentive-aligned, cryptographically enforced mechanisms (e.g., inverse-pegged synthetic tokens, dynamic supply regulation) signal a shift beyond pure detection to market-oriented mitigation.
Regulatory and community actions, including mandatory identity disclosures for project creators and proactive sharing of curated scam datasets, may serve as longer-term stabilizing forces (Sharma et al., 2023, Lin et al., 3 Mar 2024).

Overall, transaction-related rug pulls demonstrate the ongoing societal and technical need for multi-layered security, vigilance, and adaptive economic incentives in the design of decentralized financial systems.