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

Source: Dev.to

Introduction

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 the price moves against you just before execution. This is typically a Maximal Extractable Value (MEV) sandwich attack. Below we explain how sandwich attacks work and how you can protect your bots using tools like Jito bundles, Flashbots, and other best‑practice strategies.

How Sandwich Attacks Work

Sandwich attacks are a form of MEV exploitation where a malicious actor places two transactions around yours:

1. Frontrunning – The attacker detects your pending transaction (e.g., a buy order) in the public mempool and submits their own buy order first.
2. Your transaction executes – Your buy order pushes the price higher.
3. Backrunning – The attacker sells the tokens at the higher price, pocketing the difference.

The result is that you pay more for the tokens while the attacker profits. Bots that rely on public mempools are especially vulnerable because attackers can monitor these pools and execute sandwich attacks in milliseconds.

Quantifying the Impact

Example: Your bot attempts to buy 10 ETH on a DEX like Uniswap at $1,500 per ETH.

• Attacker frontruns, 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.
• Attacker sells the 10 ETH at $1,510, earning a $100 profit.

When multiplied across hundreds or thousands of trades, the losses become substantial. MEV extraction (including sandwich attacks) accounted for over $1 billion in attacker profits in 2022 alone.

Preventing Sandwich Attacks

Jito Bundles (Solana)

Jito Bundles let you submit a bundle of transactions directly to block producers, bypassing the public mempool and making your transactions invisible to attackers until they are included in a block.

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?;

Flashbots Protect (Ethereum)

Flashbots Protect (and similar services like Taichi Network) allow private transaction submission directly to miners, avoiding public mempool exposure.

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)

Competitive Gas Fees

Attackers often target low‑fee transactions because they are easier to frontrun. By setting higher, competitive gas fees you increase the likelihood that your transaction is included quickly, shrinking the window for sandwich attacks.

Key Takeaways

• Avoid public mempools: Use private transaction services (e.g., Jito Bundles, Flashbots) whenever possible.
• Monitor gas prices: Adjust fees to stay competitive and reduce exposure time.
• Batch transactions: Consolidate multiple trades into a single transaction to limit the number of visible entries.

Understanding sandwich attacks and applying these protective measures can dramatically reduce your bot’s vulnerability, improve trade efficiency, and safeguard profits.

Conclusion

Sandwich attacks remain a pervasive risk for crypto trading bots operating in public mempools. While you can’t eliminate every threat, leveraging private transaction pipelines, maintaining competitive gas fees, and batching trades will substantially mitigate exposure. Stay vigilant, keep your transactions private, and continuously optimize for speed and efficiency.

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