How to Design a Sandwich Attack Mitigation Architecture

A sandwich attack is a form of Maximal Extractable Value (MEV) where an attacker exploits the public mempool. They place one transaction before and one after a victim's pending trade, profiting from the artificial price movement. To mitigate this, you must design an architecture that either obscures transactions or executes them atomically. This guide covers three core architectural patterns: private transaction relays, commit-reveal schemes, and Flashbots-style bundles. Each approach has distinct trade-offs in cost, latency, and compatibility.

The first pattern involves using a private transaction relay like Flashbots Protect or a Taichi Network relayer. Instead of broadcasting to the public mempool, user transactions are sent directly to a trusted relay, which forwards them to block builders. This architecture requires integrating a relay RPC endpoint (e.g., https://rpc.flashbots.net) into your application's wallet connection or backend. While effective, it centralizes trust in the relay operator and may not be available on all EVM chains.

A commit-reveal scheme is a smart contract-based architecture that decouples intent from execution. Users first submit a hashed commitment of their trade details. After a delay, they reveal and execute the trade in a single transaction. This prevents frontrunning because the actual trade parameters are hidden until it's too late for an attacker to sandwich. However, this design adds user complexity (multiple transactions) and latency. Protocols like Uniswap V3 have explored similar concepts with its Time-Weighted Average Price (TWAP) orders.

The third pattern utilizes block builder bundles via systems like the Flashbots SUAVE or Eden Network. Here, your application's backend acts as a searcher, constructing a bundle that includes the user's transaction and submits it directly to a block builder. This architecture allows for atomic execution, guaranteeing the user's trade succeeds or fails as a whole, eliminating sandwich risk mid-bundle. Implementing this requires running MEV-Boost compatible software and understanding bundle construction.

When designing your architecture, key considerations include chain support (Ethereum mainnet vs. L2s), cost (relay fees or bundle tips), and user experience (additional steps or latency). For most DApps, integrating a private RPC is the simplest start. For high-value institutional trades, a custom bundle-based system may be necessary. Always simulate transactions using tools like Tenderly or Foundry's forge to test mitigation effectiveness before deployment.

A sandwich attack is a form of Maximal Extractable Value (MEV) where a malicious actor exploits the public mempool. The attacker front-runs a victim's pending transaction with their own buy order, causing the asset price to rise, and then back-runs it with a sell order to profit from the artificial price movement. To build a defense, you must first understand the transaction lifecycle on Ethereum and other EVM chains, including how transactions are broadcast, ordered in blocks by validators, and the role of the mempool as a public waiting area.

Core technical prerequisites include proficiency with Ethereum development tools. You should be comfortable with web3.js or ethers.js for interacting with the blockchain, and have experience writing and deploying smart contracts in Solidity or Vyper. Familiarity with Node.js and TypeScript is essential for building backend services that monitor the network. Understanding gas mechanics and transaction simulation is critical, as mitigation strategies often involve adjusting gas prices or using private transaction relays.

You must also grasp the specific vulnerabilities of Automated Market Makers (AMMs) like Uniswap V2/V3. Sandwich attacks target the constant product formula x * y = k. Analyze how a large trade shifts the price along the curve and how slippage tolerance settings in user transactions create the profit window for attackers. Reviewing historical attack data from services like EigenPhi or Flashbots MEV-Explore provides concrete examples of attack patterns and profitability.

Finally, architectural planning requires choosing an infrastructure stack. Decide whether your mitigation will be a user-facing browser wallet extension, a backend service for dApps, or a modification to a relay service like Flashbots Protect. Each approach has different requirements for RPC node access (e.g., Alchemy, Infura), private transaction submission, and real-time mempool monitoring using tools like Erigon or Geth with txpool subscription.

A sandwich attack targets a pending transaction in the mempool. The attacker identifies a large buy order for a token, like a swap on Uniswap. They then place two of their own transactions: one to buy the same token before the victim's transaction (front-running), and another to sell it after (back-running). This is executed using bots that monitor for profitable opportunities and pay higher gas fees to ensure their transactions are mined in the desired order.

The mechanics rely on the Automated Market Maker (AMM) pricing model. The victim's large buy increases the token's price due to slippage. The attacker's initial buy further increases the price, causing the victim to pay more. The attacker's subsequent sell then profits from the inflated price. The net effect is the victim receives fewer tokens, and the price impact is amplified, with the attacker capturing the difference as profit. This is a direct extraction of value from the victim's swap.

To execute this, attackers use sophisticated MEV (Maximal Extractable Value) bots. These bots scan the Ethereum mempool or use services like Flashbots to detect pending swaps. They calculate potential profit after factoring in gas costs and the required capital. The attack transaction bundle is often submitted via a private transaction relay to prevent being front-run by other bots, a scenario known as "MEV-on-MEV" competition.

The impact on users is significant. It results in worse execution prices (higher slippage) and a loss of funds. For the network, it contributes to congestion and higher gas fees as bots compete in priority gas auctions. While sometimes framed as a "market inefficiency," it's fundamentally a theft of user funds enabled by transparent transaction ordering.

Understanding this attack vector is the first step in designing mitigation systems. Solutions range from user-protecting protocols like CowSwap and 1inch Fusion, which use batch auctions and intent-based trading, to infrastructure-level changes like Ethereum's PBS (Proposer-Builder Separation). For developers, implementing slippage limits and using private RPC endpoints are basic defensive measures.

A sandwich attack is a form of Maximal Extractable Value (MEV) where a malicious actor exploits the public mempool. They frontrun a victim's transaction to buy an asset, let the victim's trade execute at a worse price, and then backrun to sell for a profit. To defend users, smart contracts can implement architectural changes that make these attacks unprofitable or impossible. Core strategies include using commit-reveal schemes, private transaction relays, and deadline enforcement. The goal is to reduce the predictability and profitability of frontrunning.

The commit-reveal pattern is a foundational defense. Instead of submitting a trade directly, a user first sends a transaction with a hash of their intent (the commit). After a delay, they reveal the actual trade details. This breaks the real-time predictability attackers rely on. For example, a DEX contract could require users to commitOrder(bytes32 hash) and later revealOrder(address tokenIn, uint amountIn, uint minOut). The contract verifies the hash matches the revealed data. While effective, this adds user complexity and latency, making it suitable for non-time-sensitive operations.

Enforcing strict transaction deadlines is a simpler, widely adopted mitigation. By requiring users to specify a deadline parameter—a timestamp after which the transaction reverts—contracts can limit the window for manipulation. An attacker must execute their sandwich within this short period, often just a few blocks. If the deadline is too tight for their profitable arbitrage, the attack fails. Uniswap V2 and V3 routers use this method. Contracts should always check require(block.timestamp <= deadline, "EXPIRED") and encourage wallets to set conservative deadlines.

Integrating with private transaction relays like Flashbots Protect or Taichi Network moves the transaction submission off the public mempool. The contract itself doesn't change, but the architecture must support users sending transactions via RPC endpoints that bypass public visibility. For developers, this means documenting supported RPC URLs and potentially implementing fee management for the relay's bundled transactions. While not a contract-level change per se, designing your dApp's frontend and SDK to default to a private relay is a critical architectural decision for user protection.

More advanced contracts can implement partial fill logic or slippage tolerance limits per block. Instead of reverting if the entire order can't be filled at the desired price, the contract fills what it can within the user's slippage bounds, reducing the profitable 'gap' for a sandwich. Another approach is batch auctions or fair sequencing, where orders are collected and executed at a single clearing price at the end of a block, eliminating intra-block arbitrage. Protocols like CowSwap and DEX Aggregators use variants of this model.

When designing your mitigation architecture, evaluate the trade-offs: commit-reveal adds latency, deadlines harm UX if set too tight, and private relays add centralization points. A robust design often combines several methods. Start by enforcing deadlines and maximum slippage, integrate a private relay by default, and consider commit-reveal for high-value, batched settlements. Always audit the final flow with MEV bots in mind, testing on a fork mainnet to ensure your defenses work as intended in a live environment.

A sandwich attack is a form of Maximal Extractable Value (MEV) where a malicious bot exploits the public mempool. The attacker sees a pending user swap transaction, front-runs it with their own large trade to move the price, allows the user's trade to execute at a worse rate, and then back-runs with a reverse trade to profit from the price difference. In concentrated liquidity pools, where large trades can cause significant price slippage even within a single tick, the profit potential for these attacks is amplified.

The core architectural defense is to reduce the profitability window for attackers. This is achieved by minimizing the time between transaction broadcast and execution. The most effective method is implementing a private transaction relay or private mempool, like those offered by Flashbots Protect, BloxRoute, or Taichi Network. These services submit transactions directly to block builders, bypassing the public mempool and hiding them from opportunistic bots. For a DEX, integrating with a relay service via RPC endpoint is a critical first line of defense.

On-chain, transaction ordering can be manipulated. Protocols like CowSwap and 1inch Fusion use a batch auction model. Instead of executing swaps immediately, they collect orders over a short period (e.g., a block) and settle them all at a single, uniform clearing price computed after the batch closes. This eliminates the price movement arbitrage opportunity that sandwich bots rely on, as all trades in the batch get the same final price.

Smart contract design can incorporate direct mitigations. A common technique is to enforce a maximum allowable slippage tolerance on swaps, often via a deadline and minimum output parameter. More advanced systems use Just-in-Time (JIT) liquidity, where liquidity providers add massive, concentrated liquidity in the block after seeing a user's transaction in the mempool, but before it executes. This floods the pool with depth at the execution price, drastically reducing slippage and making a sandwich unprofitable. However, JIT liquidity itself is a complex form of MEV.

For developers, implementing these features requires careful integration. A mitigation architecture might combine a private RPC for transaction submission, an on-chain require() check for user-specified slippage limits, and a backend service that monitors for potential JIT liquidity opportunities. The code snippet below shows a basic slippage check in a swap function:

solidityCopy
function swap(
    address tokenIn,
    uint256 amountIn,
    uint256 amountOutMin,
    address to,
    uint256 deadline
) external ensure(deadline) returns (uint256 amountOut) {
    // ... price calculation logic ...
    require(amountOut >= amountOutMin, "INSUFFICIENT_OUTPUT_AMOUNT");
    // ... transfer logic ...
}

Ultimately, sandwich attack mitigation is a layered defense. No single solution is perfect, but a combination of private transaction flow, favorable settlement mechanisms like batch auctions, and contract-level parameter validation can significantly reduce risk. The goal is to architect a system where the cost and uncertainty of executing an attack outweigh the potential profit, protecting end-users from value extraction.

A Time-Weighted Average Price (TWAP) order is a trading strategy that splits a large order into smaller chunks executed over a specified time interval. This is a critical tool for mitigating price impact and, more importantly, for defending against sandwich attacks. In a typical sandwich attack, a malicious bot front-runs a victim's large trade, driving the price up, executes its own trade, and then back-runs the victim's trade to capture the profit from the inflated slippage. By breaking a large order into many small, predictable trades, a TWAP system makes it economically unviable for attackers to target each individual chunk.

Designing an effective TWAP system requires a multi-layered architecture. The core components are a scheduler, an executor, and a monitoring layer. The scheduler determines the size and timing of each order chunk based on parameters like total order size, duration, and interval (e.g., execute 1% of the order every minute for 100 minutes). The executor is responsible for submitting these discrete transactions to the blockchain. Crucially, this should be done via a private transaction relayer like Flashbots Protect RPC or a similar mev-geth-compatible service to prevent the orders from being visible in the public mempool before execution.

For on-chain execution, a common pattern is to use a smart contract as the vault or executor contract. This contract holds the funds and is programmed to release only the specified chunk amount per interval. A simplified function signature might look like: function executeChunk(address dexRouter, uint256 amountOutMin) external onlyScheduler. The onlyScheduler modifier ensures only the authorized off-chain service can trigger execution. The amountOutMin is a slippage tolerance calculated per chunk, which should be tight to limit losses but not so tight that normal volatility causes frequent transaction failures.

Beyond basic scheduling, advanced mitigation involves randomization and route diversification. Instead of perfectly predictable intervals, introduce small random delays (± a few seconds). Instead of always using the same DEX or liquidity pool, dynamically route chunks across multiple venues (e.g., Uniswap, Balancer, 1inch Aggregator) based on real-time liquidity. This unpredictability increases the cost and complexity for an attacker attempting to track and front-run the order flow. Monitoring is essential: track metrics like execution price vs. market TWAP, gas costs, and failed chunks to continuously tune the strategy parameters.

When implementing, consider the trade-offs. A longer duration with more chunks reduces impact and attack risk but increases exposure to overall market price movement. Using private transaction bundles adds reliability but may incur additional costs. Always conduct simulations against historical data using frameworks like EigenPhi or Tenderly to model potential sandwich attack profitability against your strategy. The goal is not to be perfectly immune—which is impossible—but to raise the cost of attack far above the expected profit, thereby making your trade an unattractive target.

A sandwich attack is a form of on-chain frontrunning where a malicious actor exploits the public mempool. They place one transaction before a victim's pending transaction and another after it, profiting from the price impact caused by the victim's trade. This architecture focuses on integrating multiple layers of defense, from transaction-level obfuscation to protocol-level design, to protect users. The core principle is to reduce the attacker's predictability and profitability windows.

The first line of defense is transaction privacy. Instead of broadcasting a plain transaction to the public mempool, users can submit orders through a private relay or use a commit-reveal scheme. In a commit-reveal pattern, a user first sends a transaction with a hash of their intent (commit). Later, they reveal the full trade details in a second transaction. This prevents frontrunners from seeing the exact trade parameters until it's too late to act. Services like Flashbots Protect and Eden Network offer private transaction relays for Ethereum.

At the smart contract level, design choices can disincentivize attacks. Implementing minimum output thresholds or slippage tolerance checks that revert trades if the price moves beyond a set limit is fundamental. More advanced contracts use time-weighted average prices (TWAP) or batch orders to dilute the impact of a single trade. For Automated Market Makers (AMMs), integrating with Chainlink Fair Sequencing Services (FSS) or similar decentralized sequencers can enforce transaction ordering fairness, removing the miner/Maximal Extractable Value (MEV) searcher's ability to reorder.

For developers building trading interfaces or wallets, integrating these protections is crucial. A robust architecture might involve: a client-side library to generate private transactions, a backend service to route to private relays, and smart contracts with built-in slippage controls. The code snippet below shows a basic Solidity modifier for enforcing a maximum slippage tolerance, a simple but effective contract-level guard.

solidityCopy
modifier validateSlippage(uint256 minAmountOut) {
    _;
    require(
        amountOut >= minAmountOut,
        "Slippage tolerance exceeded"
    );
}

Monitoring and analytics form the final architectural layer. Tools like EigenPhi and Etherscan's MEV Inspector allow teams to track sandwich activity related to their protocol. By analyzing attack patterns, developers can adjust parameters like block inclusion delays or minimum profit thresholds for their private relay integrations. This data-driven approach ensures the mitigation architecture adapts to evolving MEV strategies, maintaining a cost-prohibitive environment for would-be attackers over the long term.

A robust mitigation architecture combines on-chain and off-chain components. Core on-chain defenses include using private mempools like Flashbots Protect or the SUAVE pre-confirmation network to hide transactions from public view. Smart contract-level solutions, such as CowSwap's batch auctions with uniform clearing prices or UniswapX's Dutch auction fill-or-kill orders, fundamentally change the execution model to remove the profitability window for front-running and back-running bots.

For developers integrating these protections, the next step is implementation. Start by routing user transactions through a service like the Flashbots Protect RPC (https://rpc.flashbots.net). For DApp builders, evaluate integrating a solver network like CoW Protocol that offers MEV protection by design. Monitor the effectiveness of your setup using block explorers that show transaction ordering (e.g., Etherscan's "Transaction Details" view) and MEV dashboards like https://explore.flashbots.net to detect any residual sandwich activity.

The landscape of MEV mitigation is rapidly evolving. Keep abreast of new developments in intent-based architectures, where users specify a desired outcome (e.g., "swap X for Y at >= Z price") rather than a specific transaction path, as seen in protocols like Anoma and UniswapX. Proactive monitoring is also crucial; implement alerting for sudden spikes in user slippage or failed transactions, which can indicate an active attack. Regularly audit and update your chosen mitigation stack as new vulnerabilities and solutions emerge in this adversarial domain.