MEV Sandwich Attack: Systemic Challenges in the Blockchain Ecosystem

With the continuous maturity of blockchain technology and the increasing complexity of the ecosystem, Maximum Extractable Value (MEV) has evolved from being initially seen as an occasional flaw caused by transaction ordering defects into a highly complex and systematic profit extraction mechanism. Among them, sandwich attacks have attracted considerable attention due to their unique operational methods, becoming one of the most controversial and destructive attack techniques in the DeFi ecosystem.

Basic Concepts of MEV and Sandwich Attacks

The Origin and Development of MEV

Maximum Extractable Value (MEV) originally refers to the additional economic benefits that block producers gain by manipulating the order of transactions and including or excluding specific transactions during the process of constructing blocks. With the development of tools such as flash loans and transaction packaging, the originally sporadic arbitrage opportunities have gradually been amplified, forming a complete profit harvesting chain. The MEV phenomenon exists not only in Ethereum but also presents different characteristics on other public chains.

Sandwich Attack Principle

Sandwich attacks are a typical operational means in MEV extraction. Attackers monitor pending transactions in the memory pool in real-time and submit their own transactions before and after the target transaction, forming the sequence "front-running transaction - target transaction - back-running transaction", achieving arbitrage by manipulating asset prices. The specific steps include:

1. Front-running: After detecting large or high-slippage trades, immediately submit buy orders to push up or depress market prices.
2. Target transaction execution: The victim's transaction is executed after the price is manipulated, resulting in a deviation between the actual transaction price and the expected price.
3. Post-trade: Following the target trade, the attacker submits a reverse trade to lock in the price difference profit.

The Evolution and Current Status of MEV Sandwich Attacks

From incidental events to systematic mechanisms

MEV attacks were initially merely an occasional phenomenon in blockchain networks. However, with the surge in trading volume in the DeFi ecosystem and the development of high-frequency trading tools, attackers have begun to build highly automated arbitrage systems. By leveraging high-speed networks and sophisticated algorithms, attackers can deploy front-running and back-running trades in a very short time, using flash loans to obtain large amounts of capital and completing arbitrage operations within the same transaction.

Characteristics of attacks on different platforms

Different blockchain networks exhibit distinct sandwich attack characteristics due to their design philosophies and transaction processing mechanisms:

• Ethereum: The public and transparent memory pool allows monitoring of pending transaction information, and attackers often pay higher Gas fees to seize the order of transaction packaging.
• Solana: Although there is no traditional memory pool, the validator nodes are relatively centralized, which may lead to transaction data leaks, allowing attackers to quickly capture target transactions.
• Binance Smart Chain: Lower transaction costs and a simplified structure provide space for arbitrage activities, with various bots using similar strategies to achieve profit extraction.

Recent Cases

In March 2025, a significant sandwich attack occurred on a trading platform. A trader suffered a loss of up to $732,000 while trading approximately 5 SOL due to the attack. The attacker exploited front-running to seize block packing rights, inserting transactions before and after the target transaction, causing the victim's actual execution price to deviate significantly from expectations.

In the Solana ecosystem, sandwich attacks are not only frequent but also new attack modes have emerged. Some validators are suspected of colluding with attackers, leaking transaction data to gain early knowledge of user trading intentions, and thereby implementing precise strikes. This has allowed certain attackers to increase their short-term earnings from tens of millions of dollars to over one hundred million dollars.

Mechanism of Sandwich Attacks and Technical Challenges

To implement a sandwich attack, the following conditions must be met:

1. Transaction Monitoring and Capture: Real-time monitoring of pending transactions in the memory pool, identifying transactions with significant price impact.
2. Priority gas fee competition: Use higher gas fees or priority fees to ensure that your transaction is executed before or after the target transaction.
3. Accurate Calculation and Slippage Control: When executing front-running and back-running trades, accurately calculate the trading volume and expected slippage, aiming to drive price volatility while ensuring that the target trade does not fail due to exceeding the set slippage.

This type of attack requires not only high-performance trading bots and fast network responses but also high miner fees to ensure transaction priority. In fierce competition, multiple bots may attempt to seize the same target transaction simultaneously, further squeezing profit margins.

Industry Response and Prevention Strategies

Prevention strategies for ordinary users

1. Set a reasonable slippage protection: Set a reasonable slippage tolerance based on current market volatility and expected liquidity conditions.
2. Use privacy trading tools: Leverage private RPC, order bundling auctions, and other technologies to hide transaction data outside of the public memory pool.

Technical Improvement Suggestions at the Ecosystem Level

1. Transaction Ordering and Proposer-Builder Separation (PBS): Limit the control of a single node over transaction ordering.
2. MEV-Boost and Transparency Mechanism: Introducing third-party relay services to enhance the transparency of the block construction process.
3. Off-chain order flow auction and outsourcing mechanism: Achieve batch matching of orders to enhance the likelihood of users obtaining the best prices.
4. Smart Contracts and Algorithm Upgrades: Utilize artificial intelligence and machine learning technologies to enhance the monitoring and predictive capabilities of abnormal fluctuations in on-chain data.

Conclusion

MEV sandwich attacks have evolved from early sporadic vulnerabilities into a systematic profit harvesting mechanism, posing a severe challenge to the DeFi ecosystem and the security of user assets. Recent cases indicate that the risk of sandwich attacks on mainstream platforms still exists and is continuously escalating. To protect user assets and market fairness, the blockchain ecosystem must work together in technological innovation, transaction mechanism optimization, and regulatory collaboration. Only in this way can the DeFi ecosystem find a balance between innovation and risk, achieving sustainable development.