How To Cook The Fragmented Rug Pull?
Minh Trung Tran, Nasrin Sohrabi, Zahir Tari, Qin Wang

TL;DR
This paper formalizes and detects fragmented rug pull attacks in DeFi, revealing their widespread use and evolving tactics that evade traditional detection methods through low-impact, multi-actor, and time-split strategies.
Contribution
It introduces a formal model for fragmented rug pulls, identifies evasive strategies, and validates these with large-scale empirical measurements showing their prevalence.
Findings
Fragmented rug pulls are widespread and operationally significant.
Attackers increasingly avoid owner-wallet participation in scams.
Large-scale data shows the evolution and scale of these evasive strategies.
Abstract
Existing rug pull detectors assume a simple workflow: the deployer keeps liquidity pool (LP) tokens and performs one or a few large sells (within a day) that collapse the pool and cash out. In practice, however, many real-world exits violate these assumptions by splitting the attack across both time and actor dimensions: attackers break total extraction into many low-impact trades and route proceeds through multiple non-owner addresses, producing low-visibility drains. We formalize this family of attacks as the fragmented rug pull (FRP) and offer a compact recipe for a slow-stewed beef special: (i) keep the lid on (to preserve LP control so on-chain extraction remains feasible), (ii) chop thin slices (to split the total exit volume into many low-impact micro-trades that individually fall below impact thresholds), and (iii) pass the ladle (to delegate sells across multiple wallets so…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Securities Regulation and Market Practices · Auction Theory and Applications
