How Sandwich Attacks Impact Trading on BNB Chain

Back in November, the BNB Chain experienced an unprecedented wave of sandwich attacks, disrupting the market for countless traders relying on crypto trading bots. These attacks, which exploit transaction ordering for profit, reveal a serious weakness in decentralized exchanges. As the crypto ecosystem continues to evolve, it’s essential to grasp these threats and apply effective countermeasures to ensure fair trading practices.

Crypto Trading Bots 101

Crypto trading bots have become quite the staple in the crypto world. Designed to automate trades and execute strategies at speeds humans can’t match, these bots operate on platforms like the BNB Chain, seizing market inefficiencies, arbitrage opportunities, and managing portfolios. But the emergence of complex attacks such as sandwich attacks presents a formidable challenge to their efficiency.

What Are Sandwich Attacks?

Sandwich attacks, or front-running, is a type of Miner Extractable Value (MEV) tactic that involves identifying a pending transaction and placing orders around it. This sequence inflates the asset’s price, causing the original trader to buy at a higher price. The attacker then sells at this inflated rate, pocketing the profit.

The Attack Sequence

1. Front-Running: The attacker places a buy order before the target transaction, causing the price to rise.
2. Target Transaction: The original transaction occurs at this inflated price.
3. Back-Running: The attacker sells after the target transaction, capitalizing on the inflated price.

These attacks are especially common in decentralized finance (DeFi) and decentralized exchanges (DEXs) due to the transparency of the mempool, which reveals transactions before confirmation.

Effects on Crypto Trading Bots

Sandwich attacks have a considerable impact on crypto trading bots on the BNB Chain. These bots, which follow set algorithms to execute trades, are easily targeted by attackers who can manipulate transaction orders. The fallout includes:

• Higher Trading Costs: Bots purchase assets at inflated prices, slicing into their profits.
• Weakened Strategy Effectiveness: Market manipulation throws a wrench into the bots’ trading strategies, leading to poor returns.
• Increased Slippage: The gap between the expected and actual trade price widens, hampering the bots’ efficiency.

In just one month, November, 43,400 traders faced sandwich attacks on the BNB Chain, illustrating the extensive reach of the problem.

How Traders Can Protect Themselves

To fend off sandwich attacks, traders should consider a few strategies:

Lower Slippage Tolerance

By setting a tighter slippage tolerance, traders can make their transactions less appealing to attackers. High slippage tolerances raise the odds of being targeted, so keeping it low (e.g., around 2%) can reduce the risk.

DEX Aggregators Use

Utilizing DEX aggregators allows for trades to be spread across multiple pools, diminishing price impact and making it harder for attackers to benefit from sandwich attacks.

Flashbot Transactions

Flashbot transactions send orders directly to miners or validators, keeping transaction data private and thus preventing manipulation. Platforms that support flashbot transactions can help users access this feature.

Favor Limit Orders and Order Management

Encouraging the use of limit orders can help reduce sandwich attack risks. These orders offer more control and predictability than market orders, decreasing vulnerability.

Custom RPC Endpoints

Using custom Remote Procedure Call (RPC) endpoints that provide MEV protection can help. These link users’ wallets to on-chain transactions while shielding them from front-running and sandwich attacks.

Monitoring and Analytics

Employing advanced monitoring tools to track transaction patterns can help to detect sandwich attacks. By recognizing the pattern of small transactions preceding and following a larger transaction, DEXs can inform users and take preventative actions.

Educational Resources

Educating users about sandwich attack risks and providing resources for thorough due diligence on markets and tokens can empower safer trading decisions, especially in the ever-changing DeFi landscape.

How Other Blockchains Compare

Ethereum

Ethereum has its own methods to mitigate sandwich attacks:
– Lower Slippage: Keeping slippage low diminishes reward potential for attackers.
– Flashbot Transactions: Orders are sent directly to miners, keeping them private.
– Limit Orders: Offer predictability, reducing exposure to sandwich attacks.
– Breaking Down Trades: Smaller transactions help avoid being targeted.

Between May 2020 and April 2022, over 450,000 sandwich attacks on Ethereum were reported, yielding substantial profits for attackers, but these strategies help reduce the impact.

BNB Smart Chain

BNB Smart Chain hasn’t been immune to sandwich attacks with increased DEX activity:
– Lower Slippage: Keeps transactions less appealing to attackers.
– DEX Aggregators: Spreads trades across pools, reducing price impact.
– Custom RPC Endpoints: Protects transactions from being visible in public mempool until confirmation.
– Limit Orders: More control means less chance of price manipulation.
– Breaking Down Trades: Smaller transactions are less attractive.
– Higher Transaction Fees: Faster confirmation reduces time for sandwich attacks.

Despite these measures, BNB Smart Chain faced a significant wave of sandwich attacks in November, particularly on PancakeSwap, affecting approximately 43,400 traders.

Solana

While details on Solana-specific strategies are sparse, sandwich attacks are also known to occur there:
– Lower Slippage: Reduces potential rewards for attackers.
– DEX Aggregators: Spreads trades across pools to reduce price impact.
– Custom RPC Endpoints: Keeps transactions private until confirmed.
– Limit Orders: More control and predictability.
– Breaking Down Trades: Smaller transactions are less appealing.

Summary

Sandwich attacks undermine the fairness of trading on the BNB Chain, allowing malicious bots to manipulate prices and affecting the profitability of both manual traders and trading bots. By implementing the strategies discussed, traders can protect themselves from these attacks and ensure their strategies remain effective. Staying vigilant and adaptable is crucial to outsmarting attackers and maintaining a secure trading environment.