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Bitquery reports that it identified 6,850,144 sandwich attacks on Solana from 30 October 2025 through 7 October 2026, with bots taking $29.7 million in gross value before venue fees, network fees and tips. The figure is an estimate under Bitquery’s detection rules—not net profit, a count of every possible attack, or a loss total assigned to every affected wallet. Bitquery says it verified its figures on 11 October 2026.
What a sandwich attack is
A sandwich attack uses the price impact of a pending swap. A bot buys the same token in the same liquidity pool before a trader’s swap, pushing the price up; the trader’s swap then executes at a worse price within its slippage limit; the bot sells afterward. The difference between the bot’s buy and sell is its gross take, before costs. Bitquery describes it as a bot buying just before a trade and selling just after.
The technique is most consequential in thin, volatile pools: a relatively small order can move the price, and a victim’s slippage setting determines how much worse an execution can be while still going through.
What Bitquery counted—and what the totals mean
| Measure | Bitquery’s reported result | How to read it |
|---|---|---|
| Attacks | 6,850,144 | Counted on Solana from 30 October 2025 through 7 October 2026. |
| Bot wallets | 2,545 | Wallets meeting Bitquery’s behavioral classification rules. |
| Victim wallets | 2,154,994 | Wallets involved in swaps classified as victims; not a count of identified people. |
| Gross take | $29.7 million, representing 309,010 SOL | Valued at the SOL price in the minute of each attack, before venue fees, network fees and tips. |
Bitquery checked costs and tips for a sample of 63 attacks. In that sample, bots retained 68% of gross value, while 18 attacks lost money after costs. That sample does not establish a population-wide net-profit rate, so the $29.7 million headline should not be read as what bots took home.
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Nor is the gross total a wallet-by-wallet loss calculation. It measures the bots’ estimated proceeds under the study’s method; it does not establish that each counted victim lost the same amount, or that all of a bot’s gross take came directly from the intervening trade.
Why Pump.fun dominates the count
Bitquery says 96% of counted attacks involved a token on Pump.fun’s launch curve or PumpSwap. Its explanation is market structure: small, volatile memecoin pools are more susceptible to price moves from individual trades. The finding describes where the detected attacks occurred; it does not by itself establish misconduct by the venue operators.
Bitquery also 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 source classifications, not identifications of the people or groups behind those wallets.
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How Bitquery identified attacks and bots
The investigation analyzed 10.7 billion successful swaps in its covered Solana exchange data during the observation window, including activity through Pump.fun, PumpSwap, Raydium, Meteora, Orca and other pools. Its attack rule 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.
To classify a wallet as a bot, Bitquery required at least 100 such sandwiches, at least 60% of them profitable, and sandwich swaps to make up at least one quarter of that wallet’s swaps. The analysis also removed certain first-slots launch activity as sniping. These thresholds help define what the reported count includes, but they do not prove that a wallet saw a particular victim’s order before it landed. Bitquery acknowledges that some wallets meeting its persistence test could instead be fast traders responding to a buying rush.
Bitquery says it validated 78 sampled sandwiches against a public Solana node, implemented its rule a second way on 25 sampled pools, and used a separate program to reproduce 39 headline figures. Those checks support the reported method, but do not eliminate its coverage and classification limits.
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Why the total is a floor, not a complete census
- Some venues, including HumidiFi and Tessera V, were not covered, and Bitquery says about 1.6% of blocks were missing from its history.
- The strict round-trip rule omits partial sells, round trips extending beyond the window, and attacks split across wallets.
- About 17,849 attacks involving tokens without a dollar price in Bitquery’s records count toward the attack total but not the dollar total.
- Including some extended round trips would raise both the count and gross take, but Bitquery cautions that much of that additional take came from later buyers rather than the trades between the bot’s buy and sell.
Wallet labels in the analysis are behavioral classifications, not identities. Bitquery says it does not attribute the wallets to named people or groups.
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How attack activity changed during the window
Bitquery compares roughly 3,400 attacks per day in late 2025 with 51,900 per day in September 2026, a roughly 15-fold increase in daily count. Swap activity also grew: the report says Pump.fun swaps per day tripled while the attack rate per swap rose about sixfold. The changing activity level matters when interpreting the overall total; it does not mean that every Solana swap, or every Pump.fun trade, was attacked.
What the slippage finding means for a swap
Among Jupiter swaps into Pump.fun memecoins during the study window, Bitquery found swaps set to 20%–50% slippage were caught in a sandwich 14 times as often as swaps set below 3%. This is an observed association in that specific subset, not proof that slippage alone caused the attacks or a prediction for any individual transaction.
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| Choice | Potential benefit | Trade-off |
|---|---|---|
| Narrower slippage | Allows less price deterioration before the swap fails. | A fast-moving market can cause a legitimate swap to fail even if no attack occurs. |
| Wider slippage | Gives a volatile swap more room to execute. | Allows a worse execution and, in the observed Jupiter subset, wider settings were associated with more sandwiching. |
Bitquery recommends keeping slippage to a few percent where practical. That is guidance, not a universally safe setting: the appropriate tolerance depends on the token’s volatility, pool depth and the risk of a failed trade.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can MEV protection stop sandwich attacks?
Not necessarily. The protection setting’s actual mechanism matters. Bitquery says common protection settings address trades within Jito bundles, while a separate study reports that many attacks against transactions intended to be protected do not occur tightly around the victim. A protection label should not be treated as a guarantee that a transaction cannot be sandwiched.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe study Bitquery cites is the 23 September 2026 arXiv preprint No Place to Hide: An Analysis on Protected Order Flow Sandwich Attacks, by Lioba Heimbach, Ozan Solmaz, Burak Öz and Christof Ferreira Torres. It covers three years across Ethereum, Solana, Tron, Base, Arbitrum and Monad, and reports 28.0 million Solana attacks against transactions intended to be protected. Its time span and detection framework differ from Bitquery’s 11-month investigation, so the totals are not directly comparable. The paper is a preprint, not a settled consensus finding.
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Practical ways to reduce exposure
- Keep slippage as low as practical for the trade, while allowing for the possibility that a volatile swap may fail.
- For a large purchase in a thin pool, consider splitting it into smaller buys; a large order can move the pool price. This may reduce price impact but does not guarantee protection.
- Check what a protection option actually does and which routes or transaction mechanisms it covers instead of assuming it blocks every form of sandwiching.
These measures manage execution trade-offs; none guarantees that a swap will avoid an attack.
How to interpret the headline
The headline is a bounded on-chain estimate: Bitquery’s rules identified millions of attacks, concentrated overwhelmingly around Pump.fun launch-curve tokens and PumpSwap, and assigned $29.7 million of gross take before costs. The result is informative about the activity Bitquery could detect in its covered data, not a complete count of sandwiching, a net earnings figure, or proof of who operated the wallets.
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