Unveiling the Internal Sniping Arbitrage of Pumpfun Token Issuance

Author: Pine Analytics

Compiled by: GaryMa Wu said blockchain

Summary

This report investigates a prevalent and highly coordinated meme token farming model on Solana: token deployers transfer SOL to "sniper wallets" allowing these wallets to purchase the token within the same block when it goes live. By focusing on the clear and provable capital chain between deployers and snipers, we have identified a set of high-confidence extraction behaviors.

Our analysis shows that this strategy is neither a coincidence nor a fringe behavior — in just the past month, over 15,000 SOL in realized profits have been extracted through this method from more than 15,000 token issuances, involving over 4,600 sniper wallets and more than 10,400 deployers. These wallets exhibit an unusually high success rate (87% of sniping profits), clean exit strategies, and structured operational patterns.

Key Findings:

• Funded sniping by deployers is systematic, profitable, and often automated, with sniping activities being most concentrated during U.S. working hours.
• Multi-wallet farming structures are very common, often using temporary wallets and collaborative exits to simulate real demand.
• Obfuscation methods are constantly upgrading, such as multi-hop funding chains and multi-signature attacks to evade detection.
• Despite its limitations, our Jump Fund Filter can still capture the clearest and most repeatable large-scale "insider" behavior cases.
• This report presents a set of actionable heuristic methods to help protocol teams and front-end developers identify, label, and respond to such activities in real time — including tracking early holding concentration, tagging wallets associated with deployers, and issuing front-end warnings to users during high-risk releases.

Although our analysis only covers a subset of block sniping behaviors in the same area, its scale, structure, and profitability indicate that the issuance of Solana tokens is being actively manipulated by collaborative networks, and the existing defenses are far from sufficient.

methodology

This analysis starts with a clear objective: to identify behaviors indicating the collaborative farming of meme tokens on Solana, particularly in cases where deployers provide funding for sniper wallets at the same block as the token launch. We divide the issue into the following stages:

1. Filter Same Zone Sniping

We first filter wallets that are sniped in the same block after deployment. This is because: Solana does not have a global mempool; one needs to know the address before the token appears on the public front end; and the time between deployment and the first DEX interaction is extremely short. Such behavior is almost impossible to occur naturally, so "same block sniping" becomes a high-confidence filter for identifying potential collusion or privileged activity.

2. Identify wallets associated with the deployer

To distinguish between skilled snipers and colluding "insiders", we tracked the SOL transfers between the deployer and the snipers before the token launch, marking only the wallets that met the following conditions: directly receiving SOL from the deployer; directly sending SOL to the deployer. Only wallets with direct transfers before the launch were included in the final dataset.

3. Associate Sniping with Token Profits

For each sniper wallet, we map its trading activities on the targeted tokens, specifically calculating: the total amount of SOL spent to buy the token; the total amount of SOL earned from selling on DEX; and the realized net profit (rather than nominal gains). This allows for precise attribution of the profits extracted from the deployer for each sniper action.

4. Measuring Scale and Wallet Behavior

We analyze the scale of such activities from multiple dimensions: the number of independent deployers and sniper wallets; the confirmed collaborative sniping occurrences in the same block; the distribution of sniping profits; the number of tokens issued per deployer; the situation of sniper wallet cross-token reuse.

5. Machine Activity Traces

To understand how these operations are carried out, we grouped the sniper activities by UTC hours. The results show that the activities are concentrated in specific time windows; there is a significant drop during the UTC late night period; this indicates that rather than being a globalization, continuous automation, it is more like a cron job or manual execution window aligned with the United States.

6. Exit Behavior Analysis

Finally, we study the behavior of deployer-associated wallets when selling sniped tokens: measuring the time from the first purchase to the final sale (holding duration); counting the number of independent sell transactions used by each wallet to exit. This distinguishes whether the wallet chooses to liquidate quickly or to sell off gradually, and examines the correlation between exit speed and profitability.

Focus on the clearest threats

We first measured the scale of block sniping in the issuance of pump.fun, and the results were shocking: over 50% of the tokens were sniped at the moment of block creation — block sniping has shifted from being an edge case to a dominant issuance model.

On Solana, participating in the same block usually requires: pre-signed transactions; off-chain coordination; or shared infrastructure between the deployer and the buyer.

Not all block snipers are equally malicious; at least two types of roles exist: "net-casting" bots — testing heuristics or small-scale speculation; and colluding insiders — including deployers providing funding for their own buyers.

To reduce false positives and highlight genuine collaborative behavior, we have implemented strict filtering in the final metrics: only counting direct SOL transfers between deployers and sniper wallets before going live. This allows us to confidently identify: wallets directly controlled by the deployer; wallets acting under the deployer's direction; wallets with internal channels.

Case Study 1: Direct Funding

The deployer wallet 8qUXz3xyx7dtctmjQnXZDWKsWPWSfFnnfwhVtK2jsELE sends a total of 1.2 SOL to 3 different wallets and then deploys a token called SOL > BNB. The 3 funded wallets were snapped up in the same block as the token was created, before it was visible to the wider market. Subsequently, they quickly sold at a profit, executing a coordinated lightning exit. This is a textbook example of swiping farmer tokens through a pre-funded sniper wallet, which is directly captured by our fund chain approach. Despite its simplicity, it has been staged on a large scale in thousands of releases.

Case Study 2: Multi-hop Funding

Wallet GQZLghNrW9NjmJf8gy8iQ4xTJFW4ugqNpH3rJTdqY5kA is associated with multiple token sniping. The entity does not directly fund the sniper wallet, but instead passes the SOL through layers 5–7 to the final sniper wallet, thus completing the sniping in the same block.

Our existing method only detects some preliminary transfers from the deployer, but fails to capture the entire chain leading to the final target wallet. These relay wallets are often "one-time use" and are only used to transmit SOL, making it difficult to associate them through simple queries. This gap is not a design flaw, but rather a trade-off in computational resources — while tracking multi-hop funding paths in large-scale data is feasible, it is highly resource-intensive. Therefore, the current implementation prioritizes high-confidence, direct connections to maintain clarity and reproducibility.

We used Arkham's visualization tools to display this longer funding chain, graphically presenting how the funds flow from the initial wallet through the shell wallet to the final deployer's wallet. This highlights the complexity of the obfuscation of the fund's origins and points the way for future improvements in detection methods.

Why focus on "direct funding and wallets that target the same blockchain"

In the remaining part of this article, we will only study sniper wallets that directly obtain deployer funds before the launch and snipe within the same block. The reasons are as follows: they contribute significant profits; they use minimal obfuscation techniques; they represent the most operationally significant subset of malicious actors; studying them can provide the clearest heuristic framework for detecting and mitigating more advanced extraction strategies.

found

Focusing on the subset of "Same Zone Sniping + Direct Funding Chain", we reveal a widespread, structured, and highly profitable on-chain collaborative behavior. The following data covers from March 15 to present:

1. Sniping funded by the deployer in the same block is very common and systematic.

a. Over the past month, more than 15,000 tokens have been directly targeted by funding wallets as soon as they go live on the blockchain;

b. Involves over 4,600 sniper wallets and over 10,400 deployers;

c. The issuance of pump.fun is approximately 1.75%.

2. This behavior is highly profitable

a. Directly funding the sniper wallet has achieved a net profit of > 15,000 SOL;

b. Successful sniping rate 87%, very few failed trades;

c. Typical single wallet earnings 1–100 SOL, with a few exceeding 500 SOL.

3. Repeated Deployment and Sniping Pointing to Farming Networks

a. Many deployers use new wallets to create dozens to hundreds of tokens in bulk;

b. Some sniper wallets perform hundreds of snipers in a single day;

c. Observed a "central-radiation" structure: one wallet funds multiple sniper wallets, all targeting the same token.

4. Sniping presents a human-centered time pattern

a. Active peaks are from UTC 14:00 to 23:00; almost at a standstill from UTC 00:00 to 08:00;

b. Aligns with US working hours, indicating it is triggered manually/cron rather than being fully automated 24 hours globally.

5. Confusion of Ownership Between Single-use Wallets and Multi-signature Transactions

a. The deployer funds multiple wallets simultaneously and signs the sniping in the same transaction;

b. These burning wallets will no longer sign any transactions thereafter;

c. The deployer splits the initial purchase into 2-4 wallets to disguise real demand.

exit behavior

To gain a deeper understanding of how these wallets exit, we break down the data along two major behavioral dimensions:

1. Exit Timing — The time from the initial purchase to the final sale;

2. Swap Count — The number of independent sell transactions used for exit.

Data conclusion

1. Exit Speed

a. 55% of the sniper shot sold out within 1 minute;

b. 85% liquidated within 5 minutes;

c. 11% completed within 15 seconds.

2. Number of Sales

a. More than 90% of sniper wallets exit with only 1-2 sell orders;

b. Very rarely adopt a progressive sell-off.

3. Profit Trend

a. The most profitable is the wallet that exits in less than 1 minute, followed by less than 5 minutes;

b. Holding for a longer period or selling multiple times may yield slightly higher average profit per transaction, but the quantity is very small, contributing limitedly to the total profit.

Explanation

These patterns indicate that the sniper funded by the deployer is not a trading activity, but rather an automated, low-risk extraction strategy:

·Buy first → Sell quickly → Exit completely.

· A single sell represents a complete disregard for price fluctuations, taking advantage of the opportunity to dump.

·A few more complex exit strategies are exceptions, not mainstream models.

Actionable Insights

The following suggestions aim to assist protocol teams, front-end developers, and researchers in identifying and addressing extraction-based or collaborative token issuance models by transforming observed behaviors into heuristics, filters, and alerts, thereby increasing user transparency and reducing risk.

Conclusion

This report reveals a continuous, structured, and high-profit strategy for Solana token issuance extraction: deployer-funded block sniper. By tracking the direct SOL transfers from the deployer to the sniper wallet, we identified a batch of insider-style behavior, utilizing Solana's high-throughput architecture for coordinated extraction.

Although this method only captures part of the block sniping within the same block, its scale and pattern indicate that this is not sporadic speculation, but rather operators with privileged positions, repeatable systems, and clear intentions. The significance of this strategy is reflected in:

1. Distort early market signals to make the tokens appear more attractive or competitive;

2. Endangering Retail Investors — They become exit liquidity without their knowledge;

3. Undermine the trust in open token issuance, especially on platforms like pump.fun that prioritize speed and usability.

To alleviate this issue, what is needed is not just passive defense, but also better heuristics, front-end early warning, protocol-level safeguards, and ongoing efforts to map and monitor collaborative behaviors. Detection tools already exist — the problem is whether the ecosystem is willing to truly apply them.

This report takes the first step: it provides a reliable and reproducible filter to identify the most obvious collusive behaviors. But this is just the beginning. The real challenge lies in detecting highly obfuscated, constantly evolving strategies and building an on-chain culture that rewards transparency rather than extraction.