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 attacks overwhelmingly involved tokens on Pump.fun’s launch curve or PumpSwap. The figure is not net profit, a count of every possible sandwich attack, or a direct measure of losses for every affected wallet.
What is a sandwich attack?
A sandwich attack happens when a bot trades around someone else’s swap in the same liquidity pool. The bot buys the token first, which can push its price up; the other trader’s swap then executes at a worse price within the slippage limit they allowed. The bot sells after that swap and keeps the difference between its buy and sell prices as gross take.
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The attack exploits transaction ordering and the price impact of a trade in a pool. A wide slippage limit can leave more room for the victim’s swap to execute at a worse price, but a low limit can also cause a swap to fail when prices move quickly.
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What does the $29.7 million figure measure?
Bitquery reports that the identified bots extracted 309,010 SOL, valued at the SOL price in the minute of each attack, for a total of $29.7 million before venue fees, network fees and tips. That is a gross estimate under Bitquery’s detection rules—not a net earnings figure or a dollar-for-dollar estimate of victims’ losses.
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Bitquery checked fees and tips for a sample of 63 attacks. In that sample, bots retained 68% of gross take on average, while 18 sampled attacks lost money after costs. The sample does not establish a population-wide net-profit rate, so it cannot be used to convert the $29.7 million headline into a reliable estimate of net earnings.
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. Those categories describe transaction activity and classifications; they do not identify the people behind the wallets.
How many attacks and wallets did Bitquery count?
For its 30 October 2025–7 October 2026 observation window, Bitquery counted 6,850,144 attacks, classified 2,545 wallets as sandwich bots and identified 2,154,994 victim wallets. It analyzed 10.7 billion successful swaps across the Solana exchange data it covered, including Pump.fun, PumpSwap, Raydium, Meteora, Orca and other pools.
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The count is specific to Bitquery’s data and rules. It should not be read as a complete census of all sandwiching on Solana, nor compared as if it used the same window and definition as a different study.
Why Pump.fun dominates the result
Bitquery reports that 96% of counted attacks involved a token on Pump.fun’s launch curve or PumpSwap. Its explanation is that small, volatile memecoin pools can be moved substantially by an individual trade, creating greater opportunity for a bot to trade around that order. This concentration is about where the counted transactions ran; it does not by itself establish wrongdoing by a venue.
Attack activity rose during the period
Bitquery compares roughly 3,400 attacks per day in late 2025 with 51,900 per day in September 2026, a 15-fold increase in the daily count. It also reports that Pump.fun swap volume per day tripled over the comparison and that the attack rate per swap rose about sixfold. The count’s growth therefore reflects both more swap activity and a higher observed attack rate, rather than a change in volume alone.
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How Bitquery identified sandwiches and bots
Bitquery’s operational rule looks for a wallet that buys a coin and later sells 99%–101% of it in the same pool, with the round trip completed within three validators’ turns. Between those trades, another wallet must trade the same coin in the same direction at a worse price. The investigation also excludes certain first-slot launch activity as sniping.
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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 the wallet’s swaps. These are publisher-defined thresholds, not a universal definition of a bot.
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 its stated method but do not remove gaps in coverage or uncertainty in wallet classification.
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What the method misses or cannot establish
- Some venues, including HumidiFi and Tessera V, were not covered, and about 1.6% of blocks were missing from Bitquery’s history. The publisher describes its count as a floor.
- The strict round-trip rule omits partial sells, trades whose round trips extend beyond the permitted window, and attacks split across wallets. Including some extended round trips would increase 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.
- About 17,849 attacks involved tokens without a dollar price in Bitquery’s records. They count toward the attack total but not the dollar total.
- On-chain evidence cannot 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 following a buying rush.
- Wallet labels are behavioral classifications; Bitquery does not attribute them to named people or groups. Venue names identify programs used by the transactions and do not, by themselves, establish misconduct by those companies.
Does a high slippage setting make a swap more vulnerable?
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 in that specific group of swaps, not proof that slippage alone caused each attack or a prediction that any individual swap will be sandwiched.
Slippage is a trade-off: a wider tolerance can help a volatile swap execute, while also allowing a worse execution price. Bitquery advises keeping the limit to a few percent when practical. There is no single setting that is safe for every token, pool or market condition; setting a limit too low may mean the transaction fails instead.
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Not necessarily. A protection setting may address a particular transaction route or mechanism without preventing every form of sandwiching. Bitquery says common protection settings address trades within Jito bundles. A separate preprint, “No Place to Hide: An Analysis on Protected Order Flow Sandwich Attacks,” by Lioba Heimbach, Ozan Solmaz, Burak Öz and Christof Ferreira Torres, was submitted to arXiv on 23 September 2026. Its abstract reports 28.0 million Solana attacks against transactions intended to be protected in a three-year study spanning Ethereum, Solana, Tron, Base, Arbitrum and Monad, and says these attacks rarely occur tightly around victims.
The preprint uses a different time span and detection framework from Bitquery’s 11-month investigation, so its 28.0 million figure is not directly comparable with Bitquery’s 6,850,144. It is also a preprint, not a settled measurement standard. Together, the findings are a reason to check what a protection feature specifically covers rather than treating a general “protected” label as a guarantee.
How to reduce execution risk when swapping
Keep slippage tight when conditions allow
Use a limit suited to the token’s volatility and the likelihood that the swap will execute. A tighter setting reduces the price movement you accept but can cause a transaction to fail during a fast market. Bitquery’s comparison supports caution with wide settings for the specific Jupiter-to-Pump.fun swaps it analyzed; it does not establish a universally safe percentage.
Consider splitting a large buy in a thin pool
Bitquery says a large order in a thin pool can move the price more and suggests splitting a large buy while keeping slippage tight. Smaller pieces may reduce the price impact of each trade, but they do not guarantee protection and may involve additional transactions and costs.
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Assess protection by its mechanism
Check which routes, transaction types or ordering mechanisms a protection setting covers. A switch that addresses one kind of ordering does not establish that the transaction is protected from every attack pattern identified on-chain.
How this count differs from a broader Solana estimate
The 28.0 million figure in the 2026 Heimbach, Solmaz, Öz and Ferreira Torres preprint covers attacks against transactions intended to be protected across a three-year, six-chain study. Bitquery’s 6,850,144 figure covers its 30 October 2025–7 October 2026 Solana dataset and its own sandwich and bot rules. Because the scope, period and detection framework differ, the totals answer different questions and should not be treated as competing counts of the same population.
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