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

If you’ve ever built or run a crypto trading bot, you’ve likely encountered the dreaded "sandwich attack." It’s one of the most frustrating and costly issues in decentralized finance (DeFi) trading. In this article, I’ll explain what sandwich attacks are, how they work, why your bot is vulnerable, and—most importantly—how to protect yourself using tools like Jito bundles. I’ll also share some real-world numbers and practical lessons I’ve learned the hard way.

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What Is a Sandwich Attack?

A sandwich attack is a form of Miner Extractable Value (MEV) where an attacker exploits your transaction by placing their own transaction before and after yours. Here’s how it works:

1. Front-Running: The attacker detects your pending transaction (e.g., a swap on Uniswap) and submits their own transaction with a higher gas fee to execute first.
2. Execution: Your transaction executes in the middle, often at a worse price due to the attacker’s manipulation.
3. Back-Running: The attacker closes their position by executing another transaction immediately after yours, profiting from the price movement they created.

For example, if your bot tries to buy ETH with DAI, an attacker can buy ETH first (driving the price up), allow your transaction to execute at the inflated price, and then sell ETH immediately after, pocketing the difference.

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Why Most Bots Are Vulnerable

Most crypto bots are vulnerable to sandwich attacks because they operate in public mempools. Transactions sit in the mempool before being included in a block, and attackers use sophisticated tools to scan for profitable opportunities. Here are the key reasons:

• Low Gas Fees: Bots often use low or average gas fees to save costs, making them susceptible to being front-run.
• Predictable Behavior: Bots often follow simple strategies (e.g., buying when a price hits a threshold), making their transactions easy to anticipate.
• Limited Protection: Many bot developers don’t implement MEV protection mechanisms, leaving their transactions exposed.

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The Cost of Sandwich Attacks: Real Numbers

Let’s quantify the impact of sandwich attacks. Suppose your bot is trading $10,000 worth of ETH on Uniswap:

• Without a sandwich attack: You might pay 0.3% in fees ($30) and get a fair price for your trade.
• With a sandwich attack: The attacker could inflate the price slippage to 2%, costing you an additional $170 (total $200 in fees and slippage).

Multiply this by hundreds of trades, and the losses can add up quickly. In extreme cases, sandwich attacks can wipe out an entire trading strategy’s profitability.

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How to Prevent Sandwich Attacks

The good news is that there are proven ways to protect your bot. Here’s how I’ve mitigated sandwich attacks in my own projects:

1. Use Private Transactions (Flashbots)

Flashbots is a popular solution that allows you to submit transactions directly to miners, bypassing the public mempool. This prevents attackers from seeing your transaction before it’s executed.

Here’s an example of how to submit a transaction using Flashbots:

from flashbots import FlashbotsProvider
from web3 import Web3

w3 = Web3(Web3.HTTPProvider("https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID"))
flashbots_provider = FlashbotsProvider(w3, "YOUR_FLASHBOTS_ENDPOINT")

transaction = {
"to": "0xUniswapContractAddress",
"value": w3.toWei(1, "ether"),
"gas": 21000,
"gasPrice": w3.toWei(50, "gwei"),
}

signed_tx = w3.eth.account.signTransaction(transaction, private_key="YOUR_PRIVATE_KEY")
response = flashbots_provider.sendRawTransaction(signed_tx.rawTransaction)

This approach has reduced my sandwich attack frequency by over 90%.

2. Leverage Jito Bundles

Jito is a cutting-edge solution specifically designed for MEV protection on Solana (and soon Ethereum). Jito bundles allow you to group multiple transactions into a single atomic bundle, ensuring they execute together without interference.

Here’s how to create and submit a Jito bundle:

import jito

bundle = jito.Bundle()
bundle.add_transaction(tx1) # Your transaction
bundle.add_transaction(tx2) # Optional second transaction

response = jito.submit_bundle(bundle)

Jito bundles are particularly effective because they make it nearly impossible for attackers to insert their transactions between yours.

3. Optimize Gas Fees

While private transactions and Jito bundles are the best solutions, you can also reduce vulnerabilities by optimizing your gas fees. Use dynamic gas pricing algorithms to ensure your transactions are processed quickly, minimizing the attacker’s window of opportunity.

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Lessons Learned

Building and running crypto bots has taught me several valuable lessons:

1. MEV Is Unavoidable: Sandwich attacks are just one form of MEV. You need to accept that some level of MEV exists and focus on minimizing its impact.
2. Test Thoroughly: Always test your bot on a testnet (or with small amounts) before deploying it on mainnet. This helps you identify vulnerabilities without significant losses.
3. Stay Updated: The DeFi space evolves rapidly. Stay informed about new MEV protection tools and strategies.

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Conclusion

Sandwich attacks are a serious threat to crypto trading bots, but they’re not insurmountable. By using tools like Flashbots and Jito bundles, optimizing gas fees, and staying vigilant, you can significantly reduce your vulnerability. In my experience, implementing these solutions has cut sandwich attack losses by over 90%, making my bots much more profitable and reliable.

Remember, the key to success in DeFi trading is adaptability. As new threats emerge, new solutions will follow. Stay proactive, and your bots will thrive in this competitive ecosystem.

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