Why Most Crypto Bots Get Sandwiched (And How to Prevent It)

As a developer who has built and deployed crypto trading bots, I’ve learned the hard way that sandwich attacks are one of the biggest threats to profitability. These attacks, part of a broader category known as Miner Extractable Value (MEV), can turn a potentially profitable bot into a losing machine. In this article, I’ll explain what sandwich attacks are, how they work, and—most importantly—how to prevent them using tools like Jito bundles.

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What Are Sandwich Attacks?

Sandwich attacks occur when a malicious actor exploits the time delay between a transaction being broadcast and its inclusion in a block. Here’s how it works:

1. Front-Running: The attacker sees your transaction in the mempool (e.g., a large buy order for a token) and places their own transaction to buy the same token before yours.
2. Execution: Your transaction executes, driving the price up.
3. Back-Running: The attacker sells the token immediately after your transaction completes, profiting from the price increase.

The result? You pay a higher price for the token, and the attacker pockets the difference.

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Real-World Impact of Sandwich Attacks

Let’s look at some numbers to understand the severity of sandwich attacks:

• Losses: On Ethereum, MEV attacks (including sandwich attacks) have extracted over $1.3 billion in value since 2020 (Flashbots data).
• Frequency: On popular DEXes like Uniswap, bots exploit up to 60% of large trades, especially during high volatility.
• Costs: For a $10,000 trade, a sandwich attack can result in losses of $200–$500, depending on slippage and token liquidity.

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Why Most Bots Are Vulnerable

Most crypto bots are vulnerable to sandwich attacks because they operate on public blockchains like Ethereum or Solana, where transactions are visible in the mempool before execution. Here are the common pitfalls:

1. Gas Price Strategy: Bots often use low gas prices to save costs, making their transactions easier to front-run.
2. Transaction Size: Large transactions are more attractive targets because they create significant price impact.
3. Lack of Protection: Few bot developers implement MEV protection mechanisms like private transactions or Jito bundles.

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How to Prevent Sandwich Attacks

The key to preventing sandwich attacks is reducing the visibility of your transactions and optimizing their execution. Here’s how:

1. Use Jito Bundles (Solana)

Jito bundles are a game-changer for Solana developers. They allow you to submit multiple transactions as a single bundle, which is executed atomically. This prevents front-running because the attacker can’t insert their transaction between yours.

Here’s an example of how to use Jito bundles in Solana:

import { Connection, Transaction, sendAndConfirmTransaction, PublicKey } from '@solana/web3.js';

const connection = new Connection('https://api.mainnet-beta.solana.com');

// Create a bundle of transactions

const tx1 = new Transaction().add(/* your instructions /);

const tx2 = new Transaction().add(/ another set of instructions */);

// Send the bundle

const bundle = [tx1, tx2];

await sendAndConfirmTransaction(connection, bundle, /* signers */);

Key benefits:

• Atomic Execution: Transactions in the bundle are executed together, preventing front-running.
• Cost Efficiency: Bundles reduce gas costs by optimizing transaction ordering.

2. Leverage Flashbots (Ethereum)

On Ethereum, Flashbots provide a similar solution by enabling private transactions. These transactions bypass the public mempool, making them invisible to attackers.

Here’s how to submit a Flashbot transaction:

import { ethers } from 'ethers';

const provider = new ethers.providers.JsonRpcProvider('https://mainnet.infura.io/v3/YOUR_PROJECT_ID');

// Create a transaction

const tx = {

to: '0xRecipientAddress',

value: ethers.utils.parseEther('1.0'),

gasLimit: 21000,

};

// Submit to Flashbots

const flashbotsProvider = new FlashbotsProvider(provider, /* signer */);

await flashbotsProvider.sendBundle([tx]);

Key benefits:

• Privacy: Transactions are not visible in the public mempool.
• Flexibility: You can bundle multiple transactions for atomic execution.

3. Optimize Slippage Tolerance

Most bot frameworks allow you to set a slippage tolerance. Keeping this value low reduces the profitability of sandwich attacks but may result in failed trades during high volatility. Experiment with different values based on market conditions.

Example in Python:

from web3 import Web3

web3 = Web3(Web3.HTTPProvider('https://mainnet.infura.io/v3/YOUR_PROJECT_ID'))

def execute_trade(token_pair, amount_in, slippage=0.01):

# Calculate minimum output based on slippage

min_amount_out = calculate_min_amount_out(token_pair, amount_in, slippage)

# Execute trade

execute_transaction(token_pair, amount_in, min_amount_out)

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Lessons Learned from Real Deployments

Here are some practical lessons I’ve learned from deploying bots:

1. Monitor Gas Prices: Use gas price APIs to track network congestion and adjust your strategy accordingly.
2. Test on Testnets: Always test your bot on testnets to simulate sandwich attacks and fine-tune your protection mechanisms.
3. Scale Gradually: Start with small trades to minimize losses while you optimize your bot.
4. Stay Updated: MEV tactics evolve constantly. Follow projects like Flashbots and Jito for the latest tools and techniques.

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Conclusion

Sandwich attacks are a pervasive threat in the world of crypto trading bots, but they’re not unavoidable. By leveraging tools like Jito bundles and Flashbots, optimizing slippage, and staying vigilant, you can significantly reduce your bot’s vulnerability to MEV attacks.

As someone who has been through the trenches, I can tell you that the effort you put into protecting your bot will pay off in the long run. At the end of the day, profitability in crypto trading isn’t just about strategy—it’s also about execution. And with the right safeguards, you can ensure your bot stays one step ahead of the sandwich attackers.

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