Research Shows Early Transaction Signals Predict DeFi Rug Pulls Within Hours
Rug pulls have become one of the defining risks of decentralized finance. For everyday users, the experience is familiar. A new token appears on a decentralized exchange, liquidity looks healthy, trading activity spikes, and then often, within hours or days, the liquidity vanishes and the token becomes worthless.
The common explanation is social engineering. Scammers market aggressively, exploit FOMO, and disappear before anyone can react. While this story is directionally true, it misses a deeper point. Rug pulls are not just narrative-driven events. They leave behind measurable, structural traces in transaction data almost from the moment a token goes live.
The research paper Rug Pull Detection on Decentralized Exchange Using Transaction Data takes this idea seriously. Instead of asking whether a smart contract contains malicious code, it asks a more practical question: how early can we tell, using only observable on-chain behavior, that a token is likely to rug?
Why Smart Contract Analysis Isn’t Enough
Much of the early work on DeFi security focused on code. Audits looked for reentrancy bugs, overflow errors, or hidden backdoors. This approach works well for certain classes of exploits, but rug pulls are different.
Rug pulls are behavioral scams. The smart contract can be perfectly functional. The exploit happens through liquidity manipulation, coordinated selling, and abandonment rather than through a technical flaw. As the paper notes, no amount of static code analysis can detect social engineering or coordinated exit behavior.
This is why many rug pulls look legitimate until it is too late. By the time users realize what is happening, the liquidity has already been drained.
The Core Insight: Rug Pulls Reveal Themselves Early
The central finding of the research is strikingly simple. Most rug pulls occur shortly after a token is created, and the transactional signals leading up to the event are often sufficient to identify the risk well in advance.
Using data from Uniswap V3, the researchers analyzed thousands of tokens and labeled them based on their post-launch behavior. They found that the majority of rug pulls reach their peak trading volume very quickly, followed by a sharp collapse in liquidity and price. This pattern is not subtle, and it is not random.
In fact, the paper shows that detection models perform best when they focus on short time windows—often within the first 8 to 20 hours after token creation. Extending the window does not necessarily improve accuracy. In some cases, it makes it worse due to class imbalance and noise.
This finding directly challenges the intuition that more data is always better. When it comes to rug pulls, speed matters more than history.
How Transaction Data Makes Behavior Visible
Rather than treating trades as isolated events, the study treats transaction data as a time series that reflects intent. Features such as trading volume in USD, number of transactions, number of holders, and the time it takes for a token to reach its maximum volume all carry behavioral meaning.
One of the most important signals identified is how quickly a token reaches peak volume. Tokens that spike rapidly are statistically more likely to be rug pulls. This aligns with incentive logic. Scammers want to extract value quickly before scrutiny increases.
The paper’s feature importance analysis reinforces this point. Maximum volume and the time required to reach it consistently rank among the strongest predictors of rug pull behavior.
This is not about predicting human psychology in the abstract. It is about observing how incentives shape behavior in a transparent system.
Why This Matters in Today’s DeFi Environment
In 2024 and 2025, DeFi has matured in surface appearance.
Interfaces are cleaner.
Liquidity is deeper.
Institutional interest is higher.
Yet rug pulls remain common, especially among newly created tokens.
What this research makes clear is that the persistence of rug pulls is not due to a lack of data. Blockchains are radically transparent. Every transaction is public. The failure lies in how that data is interpreted and operationalized for users.
Most retail users still rely on heuristics: social media sentiment, influencer endorsements, or raw volume numbers. These signals are easily manipulated. Transactional patterns, by contrast, are much harder to fake consistently over time.
The paper implicitly argues for a shift in how risk is assessed in DeFi, from narrative signals to behavioral ones.
Surface-Level Explanations vs. Structural Reality
The surface explanation for rug pulls is greed and deception. Developers exploit trust. Users chase returns. Platforms are permissionless.
The deeper structural reality is that DeFi markets expose behavior before intent becomes obvious. Rug pulls are fast because they have to be. That speed leaves a footprint.
By focusing on optimal detection windows and time-sensitive features, the research shows that rug pulls are not invisible. They are simply being measured too late or in the wrong way.
The Broader Implication: Real-Time Risk Is Possible
Perhaps the most important contribution of this work is not the specific model or algorithm used. It is the demonstration that real-time or near-real-time risk assessment in DeFi is feasible using existing data.
This opens the door to practical tools: alerts for users considering a swap, warnings integrated into DEX interfaces, or monitoring systems that flag high-risk tokens before liquidity disappears.
More broadly, it reinforces a recurring lesson in crypto research. Many of the ecosystem’s biggest risks are not hidden. They are embedded in behavior, waiting to be measured correctly.
Rug pulls feel sudden only because our tools are slow.
