Key Takeaways
- The most successful MEV bots ever, from JaredFromSubway.eth to DeezNode, have earned millions through sandwiches, arbitrage, and block-level bundle captures.
- They automate profit strategies by reordering transactions, extracting value across chains, and reshaping how decentralized markets function daily.
- MEV bots track pending transactions, simulate outcomes, and insert profitable bundles, often increasing trader costs and reducing execution quality significantly.
- Sandwich attacks showcase MEV risks clearly, as bots exploit slippage windows, inflating prices briefly and capturing value from unsuspecting traders.
The mempool never sleeps. Every second, thousands of trades queue on Ethereum, Solana, and other blockchains, each one offering profit for whoever spots it first. Maximal Extractable Value, or MEV, describes the gains an automated program or block proposer captures by reordering, inserting, or excluding transactions.
Once automated searchers appeared, MEV bots took over. They simulate pending trades in real time, monitor transaction flows, and inject their own orders at just the right moment. On Solana alone, sandwich bots pulled between US$370 million and US$500 million from users over a recent 16-month period, across more than 8.5 billion trades and over US$1 trillion in DEX volume. Originally Ethereum’s problem, MEV now infests many chains.
In this article, we look at the most successful MEV bot attacks ever recorded, from front running to sandwich attacks.
9 Biggest MEV Bot Attacks
Each example reveals how these programs affect markets, extract millions, and raise questions about fairness in decentralized finance.
1. Jaredfromsubway.eth – Ethereum – $34 Million Revenue, $6.3 Million Net Profit
Few names in MEV lore carry as much weight as jaredfromsubway.eth. This operator rose from obscurity to dominate Ethereum traffic during peak memecoin fever. Analysts traced staggering sums pouring through this address in just months.
What made it memorable was the scale and frequency. On certain days, the bot:
- Executed more than 11,000 trades.
- Spent hundreds of thousands of dollars on gas fees.
- Netted close to a million dollars in profit in under 24 hours.
The bot’s tactics relied heavily on sandwich attacks. It jumped ahead of users’ trades, pushed prices upward, and then dumped back down for profit. While costly to operate due to high gas prices, the sheer volume of opportunities kept the balance sheet green. Traders learned a hard lesson: even small swaps can become targets when someone is watching the mempool this closely.
2. Arsc – Solana – ~$30 Million In Two Months
On Solana, the arsc bot became notorious for its performance during intense periods of trading. Within only two months, it managed to earn around $30 million. The strategy was simple yet relentless.
The bot specialized in exploiting volatile tokens during their launch windows. Solana’s fast block times allowed arsc to front-run and back-run trades in rapid bursts. Observers noticed patterns where the bot would repeat sandwich transactions across dozens of pools at once, often sending validator tips that dwarfed typical network fees.
Two key elements stood out:
- Speed of execution in a network designed for high throughput.
- Coordination with validators, which secured the inclusion of its bundles.
The outcome was a machine that could drain value from trending tokens faster than human traders could react.
3. DeezNode (vpeNAL..oax38b) – Solana – 65,880 SOL In 30 Days
The DeezNode operation showcased scale over flash. In just one month, it executed over a million transactions and extracted about 65,880 SOL. That sum translated into around $13 million, based on the price at the time.
DeezNode relied on relentless volume to profit:
- Over 82,000 sandwich attacks in 30 days.
- A steady stream of arbitrages, each small in size but overwhelming in frequency.
- Validator integration that allowed transactions to land almost perfectly.
4. 2Fast – Solana – ~$1.8 Million Single Bundle Win
While many bots chase long-term gains, 2Fast pulled off a strike that traders still discuss. In one bundle, the operator transformed a relatively small stake into roughly $1.8 million.
The setup involved back-running a memecoin trade. Timing was everything. By packaging the right set of transactions, 2Fast secured a position behind a large swap and captured the price movement in an instant.
5. Peraire-Bueno Brothers – Ethereum – ~$25 Million Through Sandwich Attacks
On Ethereum, MIT graduates James and Anton Peraire-Bueno made headlines after being accused of extracting around $25 million through sophisticated sandwich attacks. Their approach combined academic precision with blockchain know-how, targeting other traders’ transactions to profit from price swings.
Investigators allege the brothers exploited vulnerabilities in the MEV Boost system, allowing them to intercept and reorder transactions. One of their main victims, Savannah Technologies, reportedly lost $13 million to the scheme.
The elements that stood out include:
- Strategic use of MEV exploitation to manipulate trade ordering.
- A deep technical understanding of blockchain systems to outmaneuver automated bots.
Their case now stands at the center of a legal debate over whether MEV-based strategies represent innovation or outright fraud.
6. B91 – Solana – 7,800 SOL In 30 Days
The B91 case study became a benchmark for crypto market participants. Over a single month, it executed more than 82,000 sandwich attacks and brought in about 7,800 SOL.
The story of B91 reveals how predictable user behavior feeds MEV. Victims included casual traders who swapped tokens without tight slippage controls. Each of their transactions created a window that B91 capitalized on.
What made the case especially valuable for analysis was the detailed data it left behind. Observers could map out how the bot sequenced trades, calculated profit margins, and distributed validator tips. That transparency helped illustrate the cumulative impact of MEV on liquidity pools.
7. Sandwich The Ripper (0x3c98d) – Ethereum – ~$20 Million Hoarded
Ethereum had its own villain in Sandwich The Ripper. This address accumulated about $20 million during a spree that combined relay manipulation with aggressive sandwiching.
Traders became wary when the bot exploited leaked order flows. Relay misconfigurations allowed it to peek at private transactions and reorder them for gain. Each move drained value from unsuspecting users while enriching the operator.
For a period, this address represented a concentrated share of all sandwich profits on Ethereum. Analysts flagged it, exchanges blacklisted it, and discussions around MEV fairness intensified.
8. 0xBAD / 0xbaDc0dE – Ethereum – ~1,101 WETH Compromise After Big Win
The hacking saga of 0xBAD, also known as 0xbaDc0dE, reads like a tragic arc. First, the bot pulled off a massive arbitrage that netted more than 800 WETH. Only hours later, an attacker exploited a vulnerability in its code and drained over 1,100 WETH.
Lessons emerged from this episode:
- Approval logic inside smart contracts must be carefully designed.
- Callback functions can create unexpected openings.
- Large sums in a contract attract immediate scrutiny from attackers.
Although the operator lost everything, the initial win and dramatic downfall remain etched in MEV history.
9. Morpho White-Hat Interceptor – Ethereum – $2.6 Million Prevented
Not every MEV story centers on profit at traders’ expense. In one instance, a white-hat operator used MEV techniques to protect funds. A vulnerability in Morpho threatened to expose user assets, but the white-hat intercepted and secured about $2.6 million until developers could resolve the flaw. It was initially reported as an exploit; however, the Morpho team confirmed that all user assets were safe.
This interception highlighted two truths:
- The same tools that steal value can also protect it.
- White-hats often act faster than manual response teams.
The event became a rare example where an MEV actor gained attention for protecting rather than extracting.
Why Are MEV Bots A Risk For Traders?
MEV bots intervene in natural trade execution. They raise effective trading costs and cause users to pay more than expected. When bots dominate, liquidity becomes harder to access without slippage. Traders feel these impacts directly in:
- Higher prices for token purchases.
- Lower prices when selling into pools.
- Larger spreads between expected and executed amounts.
The presence of MEV also changes network economics. Validators receive extra income through tips and bribes, while regular participants face higher competition.
How Do MEV Bots Spot Your Transaction?
The process begins in the mempool, where every pending trade sits in public view. Bots constantly:
- Scan transaction data in real time.
- Simulate outcomes locally before submission.
- Package bundles with precise gas bids or validator tips.
Because most transactions are transparent, bots can predict with high accuracy how a swap will move prices. Once they see profit, they act in milliseconds.
What Is A Sandwich Attack?
A sandwich attack is the most recognizable MEV tactic. It consists of three moves:
- Front-run – the bot buys before the victim’s swap.
- Victim trade executes – the price shifts upward.
- Back-run – the bot sells immediately into the higher price.
The victim ends up with a worse rate, while the bot pockets the spread. Scaled across thousands of transactions, sandwiching siphons millions of dollars from everyday traders.
Closing Thoughts
Successful MEV Bot attacks embody the ingenuity and the risks of DeFi. They use speed, precision, and coordination to harvest opportunities from public markets. Many achieve steady returns through high volume, while others strike jackpots in single bundles. Their activity influences how trades settle and forces researchers, developers, and users to adapt.
Traders who want to reduce exposure often rely on private order routing, tighter slippage limits, and platforms that minimize transaction leakage. Networks continue to debate how much MEV should exist and how it should be distributed. What remains clear is that MEV bots, with their blend of code and strategy, have written themselves into the history of decentralized finance.
