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

If you’ve ever built or used a crypto trading bot, you’ve likely encountered the frustrating phenomenon of getting “sandwiched.” Your bot places a trade, and suddenly, the price moves against you just before execution. What’s happening? You’re likely falling victim to a Maximal Extractable Value (MEV) sandwich attack. In this article, I’ll explain what sandwich attacks are, how they work, and how you can protect your bots using tools like Jito bundles. I’ll also share practical examples, code snippets, and lessons learned from my experience.

What is a Sandwich Attack?

Sandwich attacks are a form of MEV (Maximal Extractable Value) exploitation where a malicious actor “sandwiches” your transaction between two of their own. Here’s how it works:

1. Frontrunning: The attacker detects your pending transaction (e.g., a buy order) and places their own buy order before yours.
2. Your transaction executes: Your buy order pushes the price higher.
3. Backrunning: The attacker sells their tokens at the higher price, profiting from the price increase caused by your transaction.

The result? You end up paying more for your tokens, while the attacker pockets the difference. It’s a zero-sum game where bots like yours are the losers.

Why Are Bots Vulnerable?

Most crypto bots are vulnerable to sandwich attacks because they rely on public mempools where transactions are visible before being included in a block. Attackers use bots to monitor these mempools, identify profitable opportunities (like large buy orders), and execute sandwich attacks in milliseconds.

For example, if your bot attempts to buy 10 ETH on a decentralized exchange like Uniswap, an attacker can frontrun your transaction, buy ETH first, and then sell it immediately after your transaction executes. This happens thousands of times daily, and the losses add up quickly.

The Cost of Getting Sandwiched: Real Numbers

Let’s quantify the impact of sandwich attacks. Suppose your bot places a buy order for 10 ETH at a price of $1,500 per ETH. An attacker frontruns your transaction, buying 10 ETH first and pushing the price to $1,510. Your bot then buys 10 ETH at $1,510, paying $15,100 instead of $15,000. The attacker immediately sells their 10 ETH at $1,510, pocketing $100 in profit.

Multiply this by hundreds or thousands of trades, and the losses can be staggering. According to recent data, MEV extraction (including sandwich attacks) accounted for over $1 billion in profits for attackers in 2022 alone.

How to Prevent Sandwich Attacks

The key to preventing sandwich attacks is reducing your bot’s exposure to public mempools. Here are some strategies:

1. Use Jito Bundles (on Solana)

Jito Bundles are a powerful tool for avoiding sandwich attacks on Solana. They allow you to submit a bundle of transactions directly to block producers, bypassing the public mempool. This makes your transactions invisible to attackers until they’re included in a block.

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

use jito_bundles::Bundle;
use solana_sdk::transaction::Transaction;

let mut bundle = Bundle::new();
let tx = Transaction::new_with_payer(&[instruction], Some(&payer.pubkey()));
bundle.add_transaction(tx);

let bundle_hash = bundle.hash();
jito_client.submit_bundle(bundle).await?;

By submitting your transactions as a Jito Bundle, you significantly reduce the risk of being frontrun or sandwiched.

2. Use Private Mempools (on Ethereum)

On Ethereum, you can use private transaction services like Flashbots Protect or Taichi Network. These services allow you to submit transactions directly to miners without exposing them to the public mempool.

For example, using Flashbots Protect in Python:

from web3 import Web3
from flashbots import FlashbotsProvider

web3 = Web3(Web3.HTTPProvider("https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID"))
flashbots_provider = FlashbotsProvider(web3, "YOUR_FLASHBOTS_KEY")

tx = {
    "to": "0xRecipientAddress",
    "value": web3.toWei(1, "ether"),
    "gas": 21000,
    "gasPrice": web3.toWei(20, "gwei"),
}

flashbots_provider.send_transaction(tx)

3. Optimize Gas Fees

Another way to reduce the risk of sandwich attacks is by setting competitive gas fees. Attackers often target low-fee transactions because they’re easier to frontrun. By paying higher gas fees, you increase the chances of your transaction being included quickly, reducing the window of opportunity for attackers.

Lessons Learned

Here are some key takeaways from my experience battling sandwich attacks:

1. Avoid Public Mempools: Whenever possible, use private transaction services or tools like Jito Bundles to bypass public mempools.
2. Monitor Gas Prices: Always monitor gas prices and adjust your fees to stay competitive.
3. Batch Transactions: If you’re executing multiple trades, batch them into a single transaction to reduce exposure.

Conclusion

Sandwich attacks are a pervasive issue in crypto trading, especially for bots operating in public mempools. By understanding how they work and implementing protective measures like Jito Bundles or private mempool services, you can significantly reduce your bot’s vulnerability. While it’s impossible to eliminate all risks, these strategies will help you trade more efficiently and protect your profits from opportunistic attackers. Stay vigilant, keep your transactions private, and always optimize for speed and efficiency. Happy trading!

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