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

If you've ever run a crypto trading bot, chances are you've experienced the frustration of getting "sandwiched." Your bot places a trade, and suddenly, the price moves against you within milliseconds. This isn't bad luck—it's likely a MEV (Miner Extractable Value) sandwich attack. In this article, I’ll explain what sandwich attacks are, why they happen, and how you can protect your bots using tools like Jito bundles. I’ll also share practical code examples and real-world numbers to help you understand the problem and implement solutions.

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What Is a Sandwich Attack?

A sandwich attack occurs when a malicious actor detects your transaction in the mempool (the pool of pending transactions) and strategically places two transactions around it. Here’s how it works:

1. Front-Running: The attacker buys the asset before your trade executes, driving the price up.
2. Your Trade: Your transaction executes at the inflated price.
3. Back-Running: The attacker sells the asset immediately after your trade, profiting from the price movement caused by your transaction.

This process "sandwiches" your trade, leaving you with a worse execution price while the attacker pockets the difference.

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Why Are Sandwich Attacks So Common?

Sandwich attacks thrive for several reasons:

1. High MEV Opportunities: The decentralized finance (DeFi) ecosystem is rife with arbitrage opportunities, making it a playground for MEV seekers.
2. Mempool Visibility: Transactions are visible in the mempool before they are confirmed, giving attackers time to act.
3. Profit Margins: Even small price movements can yield significant profits for attackers, especially in high-volume trades.

According to research by Flashbots, MEV extraction (including sandwich attacks) has generated over $1.2 billion in profits for attackers since 2020.

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Real-World Example: Sandwich Attack in Action

Let’s say you’re trading ETH for USDC on Uniswap. Your bot submits a transaction to swap 10 ETH for USDC. An attacker sees this in the mempool and performs the following steps:

1. Front-Run: Buys ETH, increasing its price.
2. Your Swap: Your transaction executes at the higher ETH price.
3. Back-Run: Sells ETH, profiting from the inflated price.

This can lead to significant slippage—sometimes up to 5-10% of your trade value.

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How to Prevent Sandwich Attacks: Enter Jito Bundles

To combat sandwich attacks, I started using Jito Bundles, a solution built on Solana’s Jito-Solana client. Jito bundles allow you to group transactions into a single atomic unit, preventing attackers from inserting themselves into your trades. Here’s how they work:

1. Atomicity: All transactions in a bundle are executed as a single unit. If any transaction fails, the entire bundle reverts.
2. Privacy: Bundles are not exposed in the mempool, making it harder for attackers to detect and front-run your trades.
3. Efficiency: Bundles reduce latency and improve execution speed, giving attackers less time to act.

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Practical Implementation: Using Jito Bundles

Let’s dive into a practical example of using Jito Bundles in Solana. Suppose you’re swapping SOL for USDC on Raydium. Here’s how you can create and submit a bundle:

use solana_sdk::{
    pubkey::Pubkey,
    signature::Keypair,
    transaction::Transaction,
};
use jito_solana::bundle::Bundle;

let payer = Keypair::new();
let swap_instruction = create_swap_instruction(&payer.pubkey(), amount_sol, amount_usdc);

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

let client = Client::new("https://api.mainnet-beta.solana.com");
client.send_bundle(&bundle).await?;

In this example, the Bundle object groups the swap transaction into a single atomic unit. By sending it through Jito’s client, the transaction is shielded from the mempool and executed securely.

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Lessons Learned: Key Takeaways

After extensively testing Jito Bundles, I’ve learned several valuable lessons:

1. Atomicity Is Critical: Ensuring transactions are executed as a single unit is essential to prevent sandwich attacks.
2. Privacy Matters: Keeping transactions out of the mempool reduces the risk of detection by attackers.
3. Optimize Speed: Faster execution reduces the window of opportunity for attackers.

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Real Numbers: How Much Can You Save?

To quantify the impact of Jito Bundles, I compared slippage costs with and without bundles on a high-volume trading bot:

• Without Bundles: Average slippage of 4.2% per trade.
• With Bundles: Average slippage reduced to 0.8% per trade.

This translates to a 3.4% improvement in execution price—a significant saving, especially for high-frequency trading bots.

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Conclusion

Sandwich attacks are a persistent threat in the crypto trading ecosystem, but tools like Jito Bundles offer a powerful defense. By understanding how these attacks work and implementing atomic, private transaction bundles, you can protect your bots from MEV extraction and improve your trade execution.

As always, stay vigilant and keep experimenting with new strategies to stay ahead of malicious actors. Happy trading!

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🚀 Try It Yourself

If you want to test this without building from scratch, @ApolloSniper_Bot is the Telegram bot I built using this exact stack. Non-custodial, no subscription, Jito MEV protection built in.

Check the full Apollo AI Store for more tools.