Blockchain data analytics firm Bitquery has released a comprehensive new study examining whether Solana’s high-profile network performance upgrades—specifically, the shortening of block and slot times—successfully mitigated the rampant automated sniping and predatory trading that plagues popular memecoin launchpads like Pump.fun. According to the investigation, which verified its figures late last month, the structural network speedups failed to reduce rapid outside buying or eliminate malicious sandwich attacks over comparable timeframes.
The findings offer a critical reality check for layer-1 network design, illustrating that improvements in consensus latency do not inherently protect retail traders from sophisticated, automated memecoin extraction strategies. While core developers implemented aggressive slot-time reductions to enhance overall network throughput and responsiveness, automated bots adapted instantaneously, proving that speed alone is not a silver bullet against front-running and sniping.
Persistent High-Speed Acquisitions Despite Network Upgrades
The core of Bitquery’s investigation focused on tracking how quickly newly minted tokens on Pump.fun received an "outside buy"—defined as any transaction executed by a wallet other than the original creator of the coin. Across multiple sampling periods, the data revealed a striking persistence in ultra-fast acquisition behavior, even as the underlying blockchain architecture grew significantly faster.
Specifically, the study evaluated trading patterns across early August (specifically comparing samples from August 5, 12, and 19) against a later cohort from late September and early October (September 23, 30, and October 7). Both comparison phases concluded on October 7, just prior to Solana’s final scheduled network adjustment on October 9, which brought target slot times down to an aggressive 200 milliseconds.
When measuring acquisition rates within a roughly 1.3-second window from the moment a token was launched, Bitquery found virtually no meaningful change in bot and automated sniper behavior. In the earlier, slower block-time sample, roughly 53% of new tokens received an outside buy within that 1.3-second threshold. In the subsequent sample, following multiple network speed optimizations, the figure remained virtually identical at 52%.
This metric demonstrates that high-frequency trading bots and automated snipers maintained their operational efficiency regardless of the network’s underlying slot reductions. Although the dataset captured early outside buying without explicitly classifying every single swift transaction as malicious or predatory, the sheer volume and speed of these acquisitions strongly pointed to continued algorithmic dominance.
The Illusion of First-Block Metrics
A superficial glance at first-block statistics might initially suggest that network speedups successfully deterred early buyers. Bitquery’s data indicated that the share of new coins receiving an outside buy strictly within their very first block dropped significantly, tumbling from 42% in the earlier sample down to 32% in the later sample.
However, Bitquery’s analysts cautioned that this decline is largely an artifact of structural block compression rather than a true reduction in bot activity. As Solana’s validators processed slots at an accelerated pace, the duration of the first block shrank dramatically. In the earlier sample, a first block lasted approximately 0.42 seconds, whereas in the later sample, the compressed first block lasted only about 0.27 seconds.
Consequently, automated snipers and eager buyers faced a drastically narrowed temporal window to qualify for a first-block purchase. When the evaluation was expanded to cover an approximately equal elapsed timeframe—specifically the roughly 1.3-second window spanning three blocks prior and five blocks later—the apparent decline in first-block buying vanished, revealing that overall rapid accumulation remained stubbornly consistent.
The structural relationship between block duration and trading windows highlights the complexity of evaluating blockchain performance. While shorter slots successfully enhance transaction throughput and diminish the window of control that individual block leaders hold over transaction ordering, they also fundamentally alter the competitive landscape for decentralized application users and automated trading scripts.
Sandwich Patterns Dip Before Rebounding
Beyond general token sniping, Bitquery’s investigation closely monitored the prevalence of sandwich attacks—a notorious predatory practice where an automated bot detects a pending user swap, places its own buy order immediately ahead of it to artificially drive up the price, and subsequently sells right after the user’s transaction executes to capture guaranteed profit from the resulting slippage.
The trajectory of these manipulative patterns throughout the study period mapped a volatile course rather than a steady downward trend. Bitquery reported that the frequency of detected sandwich patterns—tracked per 10,000 swaps across both Pump.fun and its companion exchange, PumpSwap—hovered at approximately 30 patterns per 10,000 swaps during early August.
As network optimizations rolled out, the rate initially dipped, falling to 26 patterns per 10,000 swaps between August 22 and August 27, and declining further to 21 patterns between August 29 and September 17. However, this suppression proved temporary. As validators fully adapted to the accelerated slot cadence, the sandwich attack frequency rebounded sharply, surging to 33 patterns per 10,000 swaps during the final observation window spanning September 19 to October 7.
These empirical observations directly align with theoretical economic models published by the Solana Foundation in August. The Foundation’s economic modeling anticipated competing effects resulting from lower slot times. On one hand, reducing the time available for participants to react can physically constrain an attacker’s window of calculation. On the other hand, shorter slots can simultaneously mean that fewer competing transactions crowd the mempool, leaving more of the user’s pre-approved price tolerance—known as slippage tolerance—intact for an attacker to exploit.
If a user maintains a standard slippage allowance while network latency drops, a fast automated attacker effectively gains a wider playground to extract value within that permitted price movement. The Solana Foundation previously emphasized that lower slot times serve primarily to improve overall network latency and decentralize leader control over block building. Bitquery’s findings underscore that these architectural benefits do not inherently translate into automated consumer protection, proving that network-level performance enhancements and anti-predatory trading measures require distinct, dedicated evaluation and mitigation strategies.
