The Potential of Self-Regulation for Front-Running Prevention on DEXes

Abstract

The transaction ordering dependency of the smart contracts building decentralized exchanges (DEXes) allow for predatory trading strategies. In particular, front-running attacks present a constant risk for traders on DEXes. Whereas legal regulation outlaws most front-running practices in traditional finance, such measures are ineffective in preventing front-running on DEXes. While novel market designs hindering front-running may emerge, it remains unclear whether the market's participants, in particular, liquidity providers, would be willing to adopt these new designs. A misalignment of the participant's private incentives and the market's social incentives can hinder the market from adopting an effective prevention mechanism. We present a game-theoretic model to study the behavior of sophisticated traders, retail traders, and liquidity providers in DEXes. Sophisticated traders adjust for front-running attacks, while retail traders do not, likely due to lack of knowledge or irrationality. Our findings show that with less than 1% of order flow from retail traders, traders' and liquidity providers' interests align with the market's social incentives - eliminating front-running attacks. However, the benefit from embracing this novel market is often small and may not suffice to entice them. With retail traders making up a larger proportion (around 10%) of the order flow, liquidity providers tend to stay in pools that do not protect against front-running. This suggests both educating traders and providing additional incentives for liquidity providers are necessary for market self-regulation.

Figures (5)

• Figure 1: Execution of victim transaction $T$ in pool $X \rightleftharpoons Y$. without (cf. Figure \ref{['fig:trade0']}) and with (cf. Figure \ref{['fig:trade1']}) sandwich attack. In the presence of an attack the trader receives fewer Y-tokens $\tilde{\delta}_y<\delta_y$ while the attacker makes a profit, i.e., $a_x^\text{in}<a_x^\text{out}$.
• Figure 2: Limits on the sandwich attack size in terms of profitability (left) and slippage tolerance (right) for $f=0.3\%$. Note the vast difference in scale of the vertical axis, demonstrating that the attack is limited by the slippage tolerance.
• Figure 3: The Nash equilibrium (color shading), is dependent on the slippage tolerance and the relative benefit for $\omega=0.01$ (cf. Figure \ref{['fig:gradFeeTrade1']}) and $\omega=0.1$ (cf. Figure \ref{['fig:gradFeeTrade10']}). We set $x = 5,000,000$$X$, $y = 5,000,000$$Y$ and $f= 0.003$.
• Figure 4: Simulation of $\Delta_ F$ across both pools depending on the trader's relative benefit and the slippage tolerance for $\omega=0.01$ (cf. Figure \ref{['fig:signFee1']}) and $\omega=0.1$ (cf. Figure \ref{['fig:signFee10']}). In blue areas, the Nash equilibrium is $\text{Pool}_N$, in red areas, it is $\text{Pool}_W$. $\Delta_ F$ is cut off for better visibility and notice that the cutoff is different in the two plots. We set $x = 5,000,000$$X$, $y = 5,000,000$$Y$ and $f= 0.003$.
• Figure 5: Visualization of $\Delta_ F$ across both pools for a heterogeneous trader distribution $\psi_A ( \alpha)$ depending on mean relative benefit and the slippage tolerance. In blue areas the Nash equilibrium is $\text{Pool}_N$, in red areas, it is $\text{Pool}_W$, and in the white area in-between all liquidity distributions are Nash equilibria. $\Delta_ F$ is cut off at 0.02 for better visibility and the dotted dark blue line visualizes where $\Delta_F = 0.01$. We set $x = 5,000,000$$X$, $y = 5,000,000$$Y$, $f= 0.003$ and $\omega=0.01$.

Theorems & Definitions (28)

• Definition 1
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• Lemma 1
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• Lemma 6