1. Introduction
Decentralized Finance (DeFi) represents a transformative shift in contemporary finance, leveraging blockchain technology to furnish a decentralized, transparent platform for financial transactions. Within this domain, Decentralized Exchanges (DEX) emerge as notable entities, offering users an economical trading platform free from intermediaries. While the decentralized paradigm brings advantages such as increased transaction autonomy, heightened privacy, and reduced costs, it simultaneously unveils unique security challenges. A paramount concern is the ‘sandwich attack’, wherein attackers place their transactions around a victim’s order, capitalizing on price slippage. As the DeFi sector flourishes, reaching a market capitalization in the billions and attracting diverse investors and traders, the complexities of trading on DEXs have increased. This enhances opportunities for malefactors, compromising ordinary traders’ interests and undermining the foundational trust and fairness intrinsic to DEX. If traders perceive a risk of manipulation, they may shy away from DEX, impeding the robust and sustained evolution of the DeFi landscape.
The prevalence of sandwich attacks critically jeopardizes the fairness and transparency of DEX. If trader confidence erodes, it could trigger an exodus, reducing DEX liquidity and affecting its market efficiency and stability. Consequently, formulating a mechanism that effectively mitigates sandwich attacks and concurrently safeguards the interests of both the market and the traders has become an urgent research imperative. Moreover, it is vital that this mechanism addresses the attacks and does so without unduly impeding or delaying routine transactions. This calls for a comprehensive examination of DEX trading mechanisms, pinpointing potential vulnerabilities and formulating robust defensive strategies.
To address the outlined challenges, we advocate for an investigative approach grounded in mechanism design—a prominent subfield of economics that focuses on creating mechanisms to ensure that, despite private information, each participant’s strategic decisions converge to a socially optimal objective. Our methodology begins with a comprehensive examination of DEX trading mechanisms, highlighting susceptible areas. Building upon mechanism design theory, we then introduce an innovative trading mechanism adept at countering sandwich attacks while preserving market liquidity and efficiency. Our methodology comprises several distinct phases: 1. Data Collection and Analysis: We initiate by amassing a substantial dataset of DEX trading activities, followed by a rigorous statistical scrutiny to discern potential attack vectors and vulnerabilities. 2. Model Establishment: Using insights from the preliminary data analysis, we construct a trading model that meticulously captures the nuances of DEX transactions and potential malicious tactics. 3. Mechanism Design: Drawing from mechanism design theory, we devise a novel trading mechanism that encourages traders to disclose their authentic trading intentions transparently, effectively neutralizing malicious maneuvers. 4. Simulation and Validation: A series of comprehensive simulation experiments validate our mechanism’s robustness across diverse scenarios. Our experimental findings underscore that our proposed mechanism notably augments market fairness and transparency, ensuring optimal trading liquidity and efficiency.
The salient contributions of this work encompass the following key areas: 1. In-depth Examination of DEX Trading Mechanisms: We thoroughly investigated the DEX trading mechanisms and the associated attack strategies, which offered a rich data repository and deep insights for future research. 2. Debut of an Innovative Trading Mechanism: Our proposed mechanism, rooted in mechanism design theory, addressed the sandwich attacks while safeguarding market liquidity and efficiency—a marked breakthrough in the DEX sphere. 3. Pragmatic Implementation Guidance: Beyond theoretical discourse, our work provided actionable insights for real-world deployment. DEX operators can leverage our findings to refine their trading structures, enhancing market transparency and fairness. 4. Augmenting Mechanism Design’s Role in Finance: Furthermore, our study heralded an expanded role for mechanism design within the financial sector, especially in financial investigations.
2. Related Work
In the domain of game-theoretic mechanism design, numerous studies have addressed the challenges inherent to sandwich attacks. Heimbach and Wattenhofer provided a highly effective algorithm for traders to set the slippage tolerance. [
1]. Modern game theory applications predominantly scrutinized terrorist targeting strategies encompassing entities like businesses, officials, and civilians [
2,
3]. Within these paradigms, targets often employ divergent tactics to thwart potential threats [
4,
5]. Noteworthy contributions by Sandler et al. and Zhuang et al. utilized game theory to elucidate equilibrium strategies for both attackers and defenders, focusing on optimal resource allocation against terrorism and natural disasters [
6,
7]. Similarly, Liang et al. presented an exhaustive survey of game-theoretic solutions targeting network security, bifurcating the strategies into attack–defense analyses and security metrics [
8]. Zonouz et al. pioneered the Response and Recovery Engine as an innovative response mechanism [
9]. Shamshirband et al. invoked a cooperative game-theoretic approach, enhanced with fuzzy q-learning, to detect and mitigate intrusions in wireless sensor networks [
10]. Yuan et al. further postulated optimal criteria for cyber defenders and DoS attackers, leveraging optimal control paradigms [
11]. This Special Issue spotlighted the cyber–physical security facets of Networked Control Systems and encapsulated state-of-the-art developments within system theory and decision sciences applied to this rapidly evolving domain [
12].
Transitioning to the blockchain domain, Cai et al. critically evaluated blockchain-driven reputation systems, emphasizing vulnerabilities like ballot-stuffing and bad-mouthing [
13]. Tosh et al. contextualized block withholding attacks within blockchain cloud architectures, accounting for disparate pool reward dynamics [
14]. A novel threat vector, termed the balance attack, was introduced by Natoli et al., targeting forkable blockchain infrastructures [
15]. Subsequent contributions by Budish et al. and Dey et al. delved into the economic constraints of Bitcoin and proffered machine learning frameworks to starve off a majority of blockchain-centric threats [
16,
17]. For bolstered security, Rathore et al. advocated for an integrated architecture merging Software Defined Networking with blockchain. This approach amplified the prowess for detecting threats within IoT ecosystems [
18].
Based on previous research, this paper enhances the result of the extant literature [
2,
4,
5,
19,
20,
21,
22,
23] by introducing a bespoke game-theoretic mechanism, specifically crafted to neutralize sandwich attacks within market-centric environments.
3. Preliminary Work
In the context of the swift evolution of blockchain technology and DeFi, DEX have emerged as a focal component, attracting considerable attention. Nevertheless, the inherent openness and transparency of DEX inadvertently provide avenues for malevolent actors, with sandwich attacks standing out as the most prevalent one. In this section, we elucidate the intricacies of the sandwich attack, review the existing literature, and emphasize the significance of mechanism design in DEX.
3.1. Principles of the Sandwich Attack
At its core, the sandwich attack involves an attacker strategically positioning their transactions around a victim’s transaction to capitalize on price variances. The attacker initially inflates the asset’s price with the transaction (1), waits for the victim’s transaction to materialize and subsequently deflates the asset’s price with the transaction (3), thereby securing a profit.
- (1)
Scenario without an Attack:
The victim sent a transaction (2) using of x transformation of of y; the underlying Automated Market Maker (AMM) market starts from ; the market becomes a state without any unexpected price sliding points caused by the attack.
- (2)
Scenario with an Attack: The attacker’s initial state is
. They initiate the front-running transaction (1) and the subsequent transaction (3) to launch a sandwich attack. Let
be defined as the attack cost just sufficient for a single sandwich attack.
The attacker exchanges
of
x for
of
y. Starting from the underlying AMM market state
, the market transitions to state
.
Due to the front-running transaction, when executing the (2) transaction, the market state is no longer the initially assumed
, but
. Modifying the original (2) transaction formula by replacing
with
, we obtain the new market state
.
After the victim’s transaction is executed, the attacker’s subsequent transaction (3) is executed. The attacker exchanges
of
Y for
X. Starting from the market state
, the market transitions to state
. Ultimately, the attacker obtains a profit of
in
X. The attacker’s profit is calculated by subtracting the attack cost from the
X currency amount obtained after the attack, which is written as follows:
3.2. Introduction to Mechanism Design Game Theory
Mechanism design game theory, a prominent subfield of economics, investigates the formulation of mechanisms that ensure that the participants’ optimal strategies coincide with the designer’s objectives. Within the sphere of DeFi, such designs are indispensable. This is primarily due to the information asymmetry between the market and its participants, which holds the potential for engendering malicious actions. Ideally, if the market possessed complete knowledge of a participant’s intentions—including potential malevolent plans—it could proactively design deterrent strategies. Yet, participants frequently withhold full disclosure, fearing privacy breaches or strategic exposure. This discrepancy presents a critical challenge: how can one construct a mechanism that safeguards participant privacy and mitigates malevolent actions? The answer lies in mechanism design game theory, particularly in its revelation principle. This principle asserts that, for every Bayesian Nash equilibrium, a corresponding direct mechanism exists wherein participants genuinely reveal their information. Consequently, one can effectively thwart malevolent endeavors by crafting a direct mechanism that incentivizes participants to disclose their intentions honestly. This study conceptualizes the market as the principal and the participants as the players. The principal’s mandate is to curate a mechanism encouraging the players to transparently convey their data, enabling the market to strategize and optimize its returns.
3.3. Introduction to the Problem
To describe this problem more concretely, we introduce some mathematical notations. We use R to represent the market and W to represent the set of workers. Each worker wi has a type , where Ci is the worker’s total assets, and Ti is his intention to attack. The types of all the workers form a type of space . The information that worker wi reports to the market is represented as , where is the worker’s strategy, a function of the worker’s true type zi. The strategies of all the workers form a strategy space . The market produces an output yi based on these strategies, and all the outputs form an output space . In this framework, our goal is to design a mechanism, a mapping from strategy space G to output space Y. The devised mechanism ought to meet two essential criteria: firstly, it must optimize the market’s utility, and, secondly, it must ensure that the most strategic approach for the participants is to relay their information truthfully. Specifically, is the requester’s strategy, and is the mechanism strategy space. is seen as the utility of the market, while is seen as the utility of the workers.
3.4. General Framework to Counteract Sandwich Attacks
Given the escalating occurrence of sandwich attacks in the DeFi landscape, there is an imperative need for a holistic and robust strategy to neutralize them. A proficient strategy should not only detect and preempt these assaults but also guarantee that the DeFi environment retains its transparency, decentralization, and user-centricity.
Our proposed holistic strategy addresses the technical nuances and behavioral dynamics of sandwich attacks. Technologically, we incorporate sophisticated surveillance tools harnessing machine learning methodologies to discern anomalous trading activities instantaneously. These instruments are adept at pinpointing impending sandwich attacks, and facilitating swift counteractions.
Moreover, we champion the incorporation of transaction latency protocols. The attacker’s anticipated trajectory can be unsettled by instating a minor, randomized latency to every transaction. Even a slight delay could dissuade potential attackers while posing minimal disruption to regular traders.
Behaviorally, our strategy underscores the importance of trader education. By equipping traders with comprehensive insights into sandwich attacks, through resources and tools, they can be empowered to make judicious choices and adopt prudent trading habits. This encompasses instructive guides, illustrative case studies of sandwich attacks, and trading safety protocols.
Lastly, we advocate a communal approach to tackling these onslaughts. Envisioning a platform where users can flag dubious activities, recount their encounters, and brainstorm solutions can cultivate a communal front against adversarial elements. Harnessing the shared expertise and alertness of the DeFi populace can usher in a fortified, resilient ecosystem.
3.5. The Rise of DeFi
DeFi has emerged as a transformative paradigm within the financial landscape, heralding a transparent, permissionless, open-source financial service ecosystem. Operating devoid of central authority, this model ensures global inclusivity in accessing financial services. The catalyst propelling DeFi’s meteoric ascent is the Ethereum blockchain, which facilitates the deployment of smart contracts—self-executing contracts with predetermined conditions. These contracts have enabled many novel, transparent, and censorship-resistant financial offerings.
3.6. The Role of DEX in DeFi
DEX within the DeFi spectrum signify a profound evolution in finance. Capitalizing on blockchain technology, DeFi platforms provide financial services, bypassing conventional intermediaries such as banks. This democratization broadens the accessibility of financial instruments to an expansive global audience.
Several dynamics underpin DeFi’s ascendancy. Primarily, the innate transparency and robust security features of blockchain render it a compelling alternative to established financial architectures. Activities are cataloged on a public ledger ensuring transparency, while cryptographic methodologies undergird its security and integrity.
Furthermore, DeFi introduces groundbreaking financial instruments and services absent in classical financial systems. Mechanisms from yield farming to liquidity mining permit users’ novel avenues for asset appreciation, a departure from conventional modalities. Compounded by the automation capabilities of smart contracts, operational costs diminish while efficiency soars.
Nevertheless, DeFi’s exponential growth is not devoid of hurdles. Given its emergent phase, many platforms have yet to weather real-world exigencies, leading to vulnerabilities—with sandwich attacks epitomizing such pitfalls. As the DeFi domain advances, it is imperative to confront these challenges robustly, maintaining the integrity and trustworthiness of the ecosystem.
The prospects of DeFi are vast, heralding a more inclusive, agile, and lucid financial framework. However, the risks stemming from malicious exploits and system vulnerabilities must be preemptively addressed to harness their full potential.
3.7. Sandwich Attacks: A Growing Concern
Sandwich attacks, an emergent form of market manipulation, have raised substantial concerns within the DeFi realm. These attacks capitalize on the transparent sequencing of blockchain transactions, enabling malicious entities to leverage other traders’ actions preemptively. At its core, an attacker identifies an impending significant transaction, strategically positioning their trades before and after the target, thereby “sandwiching” it. This strategy facilitates price manipulation in favor of the attacker, potentially yielding substantial profits.
For example, attackers would buy the asset that users are exchanging, e.g., using Chainlink to exchange for Ether (ETH), knowing that the price of ETH is increasing. The criminals then purchase ETH at a lower price so that the victim can purchase it at a higher price. The attacker then sells the ETH at a higher price. This affects the amount of ETH the initial user will receive. Since the attackers managed to execute the order at the price they wanted, the cost of the following trade would be higher. This causes the price of ETH to increase, allowing attackers to profit by trading traders back and forth and artificially creating a price increase.
The underpinnings of sandwich attacks are intricately linked to blockchain transaction processing. Given that transactions are appended to the blockchain in aggregated “blocks”, a temporal window emerges wherein attackers discern pending transactions and strategize. Herein, the deterministic transparency of blockchain, typically its forte, is paradoxically a vulnerability.
As the DeFi ecosystem amplifies in traction and valuation, the lucrative allure of successful sandwich attacks intensifies, magnetizing malicious entities keen on exploiting such weak points. This trajectory not only imperils individual traders financially but also erodes trust in decentralized frameworks. For DeFi to fully manifest its promise, it is essential to confront and neutralize such challenges proactively.
3.8. The Need for Mechanism Design in DeFi
The inherent decentralization of DeFi platforms demands avant-garde strategies to safeguard security and equity. Colloquially termed “reverse game theory”, mechanism design involves sculpting a game with meticulous rules tailored to yield specific outcomes, even amidst participants pursuing self-centered objectives. Within DeFi, this translates to architecting infrastructures such that overarching system equity and integrity prevail irrespective of participants’ potentially self-serving or malevolent actions.
DeFi’s decentralized framework implies the absence of a centralized arbitrator to instate rules or penalize ill-intentioned players. This void in centralized governance renders traditional fairness and security safeguards, such as regulations or supervisory entities, impotent. Consequently, the inherent system protocols must be resilient to counteract malevolent conduct.
By harnessing mechanism design, we can cultivate DeFi platforms where optimal strategies for individual actors (traders, liquidity contributors, or prospective adversaries) synchronize with the platform’s collective optimum. This might encompass architecting fee models that dissuade sandwich attacks, incentivizing user-driven suspicious activity reporting, or pioneering trading algorithms impervious to recognized threats.
Moreover, as the value managed by DeFi platforms escalates, so do the stakes. An isolated vulnerability or systemic design oversight can trigger monumental fiscal setbacks and reputational dents. Ergo, integrating mechanism design within DeFi transcends mere academic contemplation—it crystallizes as a pragmatic imperative, anchoring the enduring credibility and reliability of decentralized finance constructs.