Bitquery reports that it detected 6,850,144 sandwich attacks on Solana from 30 October 2025 through 7 October 2026, with bots taking $29.7 million gross—before venue fees, network fees and tips. The attacks overwhelmingly involved tokens on Pump.fun’s launch curve or PumpSwap. The figures describe activity detected under Bitquery’s rules, not net bot profit, every possible sandwich attack, or a dollar loss assigned to each affected wallet.
What is a Solana sandwich attack?
A sandwich attack is a sequence of swaps around another trader’s swap in the same liquidity pool. A bot buys a token first, pushing its price up; the trader’s swap then executes at a worse price, within the slippage limit the trader accepted; the bot sells afterward. The difference between the bot’s buy and sell is its gross take before costs.
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As Bitquery puts it, “A sandwich attack is when a bot buys a coin just before your trade and sells it just after.” That describes the pattern, but identifying such a pattern on-chain does not prove that a bot saw a particular trader’s order before it was included in a block.
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What did Bitquery count, and what does $29.7 million mean?
Bitquery’s figures, checked on 11 October 2026, cover its Solana exchange data from 30 October 2025 through 7 October 2026. The investigation analyzed 10.7 billion successful swaps across covered exchange activity, including Pump.fun, PumpSwap, Raydium, Meteora, Orca and other pools.
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| Measure | Bitquery’s reported result | How to read it |
|---|---|---|
| Detected sandwich attacks | 6,850,144 | Attacks identified under the investigation’s criteria during the stated window. |
| Wallets classified as sandwich bots | 2,545 | Behavioral wallet classifications, not identifications of people or organizations. |
| Victim wallets | 2,154,994 | Wallets counted in the detected attacks; this is not a count of distinct people or a direct measure of each wallet’s loss. |
| Gross take | 309,010 SOL, valued at $29.7 million | Bitquery valued SOL at its price in the minute of each attack. The amount is before venue fees, network fees and tips. |
The $29.7 million is therefore not a net-profit estimate. Bitquery checked fees and tips in a sample of 63 attacks: bots retained 68% of gross in that sample, while 18 sampled attacks lost money after costs. Those sample results do not establish the population-wide net earnings of the bots.
Bitquery attributes 39% of the gross take to trades by bot wallets, including snipers and other sandwich bots, under its wallet-classification criteria. It reports that $18.1 million came from other wallets. These are classifications of on-chain behavior, not proof that a person knowingly participated in an attack.
Why did Pump.fun account for most detected attacks?
Bitquery says 96% of the attacks it counted involved a token on Pump.fun’s launch curve or PumpSwap. The investigation links this concentration to small, volatile memecoin pools: when liquidity is thin, a trade can move the token’s price substantially, creating more room for a sequence of trades around it.
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The count also grew as swap activity increased. Bitquery compares roughly 3,400 attacks per day in late 2025 with 51,900 per day in September 2026, a 15-fold increase. It says Pump.fun swap activity tripled over the comparison while the attack rate per swap rose about sixfold. The daily counts alone do not show that any single user’s trade faced a 15-fold increase in risk.
How did Bitquery decide what counted?
Attack and bot criteria
For an attack, Bitquery required a wallet to buy a coin and later sell 99%–101% of it in the same pool, with the round trip fitting within three validators’ turns. Between those trades, another wallet had to trade the same coin in the same direction at a worse price. Bitquery’s bot classification required at least 100 such sandwiches, at least 60% of them profitable, and sandwich swaps making up at least one quarter of that wallet’s swaps. The analysis also removed certain first-slots launch activity as sniping.
These thresholds make the result reproducible within the stated framework, but they also define what the investigation can see. Bitquery cautions that on-chain data cannot prove whether a wallet saw a victim’s order before it landed; some wallets selected by the persistence test could instead be fast traders following a buying rush.
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Coverage and counting limits
Bitquery describes its total as a floor. Its history missed about 1.6% of blocks, and some venues, including HumidiFi and Tessera V, were outside its coverage. The strict rules also omit partial sells, round trips that extend beyond the allowed window, and attacks split across wallets. Counting some extended round trips would increase both the count and gross take, but Bitquery cautions that much of the additional take came from later buyers rather than the trades between the bot’s buy and sell.
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What does slippage have to do with sandwich risk?
Slippage tolerance is the amount a trader allows the execution price to move from the quoted price. A wider limit can help a swap complete in a fast-moving market, but it also permits a worse execution. A narrow limit reduces that room while making a transaction more likely to fail if the price moves before it executes.
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In its study window, Bitquery found that Jupiter swaps into Pump.fun memecoins set to 20%–50% slippage were caught in a sandwich 14 times as often as swaps set below 3%. This is an observed association for those swaps and bands, not proof that slippage alone caused the difference or a prediction for an individual transaction. It does not establish a universally safe setting.
| Choice | Potential benefit | Trade-off |
|---|---|---|
| Narrower slippage | Leaves less permitted room for a worse execution. | A volatile market may move outside the limit, causing the swap to fail. |
| Wider slippage | Can make execution more likely when prices are moving quickly. | Allows the swap to complete at a worse price, increasing the room an attacker may exploit. |
Bitquery recommends keeping slippage to a few percent when practical. That is guidance, not a guarantee: the appropriate limit depends on the token’s volatility, pool liquidity and a trader’s willingness to accept a failed transaction versus a worse execution.
Does MEV protection stop sandwich attacks?
Not necessarily. Bitquery says common protection settings address trades within Jito bundles. A separate preprint by Lioba Heimbach, Ozan Solmaz, Burak Öz and Christof Ferreira Torres, submitted to arXiv on 23 September 2026, reports 28.0 million attacks against transactions intended to be protected on Solana. Its authors say those attacks rarely occur tightly around the victim.
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The preprint covers three years and six chains—Ethereum, Solana, Tron, Base, Arbitrum and Monad—and uses a different detection framework from Bitquery’s 11-month Solana investigation. Its 28.0 million figure is not directly comparable with Bitquery’s 6,850,144. It is a preprint, not a final peer-reviewed finding. Together, the sources show why a protection switch should be judged by the mechanism it promises to use, rather than treated as a guarantee against every form of harmful transaction ordering.
What can a trader do when swapping a thinly traded token?
- Set slippage as narrowly as is practical. A tighter limit can reduce the price movement you accept, but may cause a swap to fail in a volatile market.
- Consider splitting a large buy. Bitquery advises breaking up a large order in a thin pool because a large trade can move the price more. This may reduce the impact of one transaction, but it is not a guarantee against sandwiching.
- Check what a protection setting actually covers. A setting focused on a particular bundle or route is not necessarily protection from every attack pattern.
- Keep the risk in perspective. Bitquery’s aggregate counts do not establish that a specific swap was targeted, nor do they show a specific wallet’s loss.
What the headline does—and does not—establish
Bitquery’s investigation documents substantial detected sandwich activity in its covered Solana data, concentrated around Pump.fun launch-curve tokens and PumpSwap. Its $29.7 million is a gross estimate under a defined method, before costs. It is neither a complete census of all sandwiching nor a population-wide net-profit figure, and the wallet labels do not identify the people behind the trades.
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