MEV Sandwich Attack: The Invisible Threat to the Decentralized Finance Ecosystem

In today's world where blockchain technology is becoming increasingly mature and ecosystems are growing more complex, MEV (Maximum Extractable Value) has evolved from sporadic vulnerabilities caused by transaction ordering flaws into a highly complex and systematic profit extraction mechanism. Among these, sandwich attacks have become one of the most controversial and destructive attack methods in the DeFi ecosystem by cleverly utilizing transaction ordering rights to insert proprietary transactions before and after target transactions, manipulating asset prices to achieve low buy and high sell arbitrage.

1. Basic Concepts of MEV and Sandwich Attacks

The Source and Technological Evolution of MEV

MEV initially refers to the extra economic benefits obtained by miners or validators during the block construction process through manipulating transaction order and inclusion or exclusion rights. Its theoretical basis lies in the transparency of blockchain transactions and the uncertainty of transaction ordering in the mempool. With the development of tools such as flash loans and transaction bundling, sporadic arbitrage opportunities have gradually been amplified, forming a complete profit harvesting chain. MEV has evolved from a sporadic event into a systematic and industrialized arbitrage model, existing not only in Ethereum but also presenting different characteristics on other public chains.

The principle of sandwich attacks

Sandwich attacks are a typical operation method in MEV extraction. Attackers monitor memory pool transactions in real time and submit transactions before and after the target transaction, forming the sequence "front-running - target transaction - back-running," achieving arbitrage through price manipulation. The core steps include:

1. Front-running: After detecting large or high-slippage trades, immediately submit buy orders to push up or down the market price.
2. Target trade ambush: The target trade is executed after the price is manipulated, resulting in a significant deviation between the actual transaction price and the expected one.
3. Post-Trade: Submit a reverse trade immediately after the target trade to lock in the price difference profit.

This operation is akin to "sandwiching" the target transaction between two other transactions, hence it is called a "sandwich attack".

2. The Evolution and Current Status of MEV Sandwich Attacks

From sporadic vulnerabilities to systemic mechanisms

MEV attacks initially occurred sporadically and on a small scale. With the surge in trading volume in the DeFi ecosystem and the development of high-frequency trading bots and flash loans, attackers have built highly automated arbitrage systems, transforming the attack method into a systematic and industrialized arbitrage model. Utilizing high-speed networks and sophisticated algorithms, attackers can deploy front-running and back-running trades in a very short time, using flash loans to acquire large amounts of funds and completing arbitrage in the same transaction. Currently, profits from a single trade can reach hundreds of thousands or even millions of dollars, marking the evolution of the MEV mechanism into a mature profit harvesting system.

Attack patterns of different platform characteristics

Different blockchain networks exhibit distinct sandwich attack characteristics due to differences in design philosophy, transaction processing mechanisms, and validator structures.

• Ethereum: The public and transparent memory pool allows all pending transaction information to be monitored, and attackers often pay higher Gas fees to prioritize their transactions in the packing order. To address this issue, mechanisms such as MEV-Boost and proposer-builder separation have been introduced to reduce the risks of a single node manipulating transaction ordering.

• Solana: Although there is no traditional memory pool, the validator nodes are relatively centralized, and some nodes may collude with attackers to leak transaction data in advance, allowing attackers to quickly capture and exploit target transactions, leading to frequent sandwich attacks with significant profits.

• Binance Smart Chain: The lower transaction costs and simplified structure provide space for certain arbitrage activities, and various bots can similarly adopt strategies to achieve profit extraction.

The differences in this cross-chain environment lead to unique characteristics in attack methods and profit distribution across different platforms, while also raising higher requirements for prevention strategies.

Latest case

On March 13, 2025, in a transaction that took place on a certain DEX, a trader lost assets worth up to $732,000 when conducting a trade valued at approximately 5 SOL due to a sandwich attack. The attacker leveraged front-running to seize block packaging rights, inserting transactions before and after the target transaction, causing the victim's actual transaction price to significantly deviate from expectations.

In a certain public chain ecosystem, sandwich attacks are not only frequent but also new attack patterns have emerged. Some validators are suspected of colluding with attackers by leaking transaction data to gain early knowledge of user transaction intentions, thereby implementing precise strikes. This has caused some attackers on the chain to increase their profits from tens of millions of dollars to over a hundred million dollars in just a few months.

These cases indicate that MEV sandwich attacks have become a systematic and industrialized phenomenon accompanying the increasing transaction volume and complexity of blockchain networks.

3. The Operating Mechanism and Technical Challenges of Sandwich Attacks

As the overall market trading volume continues to expand, the frequency of MEV attacks and the profit per transaction are on the rise, with some platforms seeing the cost-to-income ratio of sandwich attack transactions reaching a high level. The following conditions must be met to implement a sandwich attack:

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: Using higher gas fees or priority fees to compete to package one's own transactions into blocks ahead of others, ensuring execution before and after the target transaction.

3. Accurate Calculation and Slippage Control: When executing pre-trade and post-trade transactions, accurately calculate the trading volume and expected slippage, ensuring that price fluctuations are encouraged while ensuring that the target trade does not fail due to exceeding the set slippage.

Implementing such an attack requires not only high-performance trading bots and fast network responses but also the payment of high miner bribe fees to ensure transaction priority. These costs constitute the main expenses for the attacker, and in intense competition, multiple bots may simultaneously attempt to seize the same target transaction, further compressing profit margins. These technical and economic barriers continuously prompt attackers to update their algorithms and strategies, while also providing a theoretical basis for the design of preventive mechanisms.

IV. Industry Response and Prevention Strategies

Prevention strategies for ordinary users

1. Set reasonable slippage protection: Based on current market volatility and expected liquidity conditions, set a reasonable slippage tolerance to avoid trade failures caused by setting it too low, and to prevent being maliciously squeezed by setting it too high.

2. Use privacy trading tools: By utilizing private RPC, order batching auctions, and other technical means, hide transaction data outside of the public memory pool to reduce the risk of attacks.

Suggestions for technical improvements at the ecosystem level

1. Transaction Ordering and Proposer-Builder Separation (PBS): By separating the responsibilities of block construction and block proposing, it limits a single node's control over transaction ordering, reducing the likelihood that validators can exploit ordering advantages for MEV extraction.

2. MEV-Boost and Transparency Mechanism: Introducing third-party relay services and solutions like MEV-Boost to make the block building process open and transparent, reducing reliance on a single node and enhancing overall competitiveness.

3. Off-chain order flow auction and outsourcing mechanism: By leveraging the outsourcing of orders and the order flow auction mechanism, bulk order matching is achieved, increasing the likelihood of users obtaining the best prices, while making it difficult for attackers to operate individually.

4. Smart Contracts and Algorithm Upgrades: Utilizing artificial intelligence and machine learning technologies to enhance real-time monitoring and predictive capabilities for abnormal fluctuations in on-chain data, helping users to proactively avoid risks.

As the DeFi ecosystem continues to expand, trading volume and complexity are continuously increasing, and MEV and related attack methods will face more technical countermeasures and economic games. In the future, in addition to improving technical means, how to reasonably allocate economic incentives while ensuring decentralization and network security will become an important issue of common concern in the industry.

V. Conclusion

MEV sandwich attacks have evolved from occasional vulnerabilities into a systemic profit harvesting mechanism, posing severe challenges to the DeFi ecosystem and user asset security. The latest cases and data from 2025 indicate that the risks of sandwich attacks still exist and are continuously escalating, whether on mainstream DEXs or other public chain platforms. To protect user assets and market fairness, the blockchain ecosystem needs to work together on technological innovation, trading mechanism optimization, and regulatory collaboration. Only in this way can the DeFi ecosystem find a balance between innovation and risk, achieving sustainable development.