Exit the Liquidity Machine: Unveiling the Internal Sniping Arbitrage of Pumpfun Token Issuance

Author: Pine Analytics

Compiled by: GaryMa Wu Says Blockchain

Abstract

This report investigates a prevalent and highly coordinated meme token farming model on Solana: token deployers transfer SOL to "sniper wallets," enabling these wallets to purchase the token within the same block as its launch. By focusing on the clear, provable funding chain between deployers and snipers, we identify a set of high-confidence extraction behaviors.

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

Key Findings:

• Deployer-funded sniping is systematic, profitable, and often automated, with sniper activity most concentrated during U.S. working hours.
• Multi-wallet farming structures are very common, often using temporary wallets and coordinated exits to simulate real demand.
• Obfuscation techniques are continually evolving, such as multi-hop funding chains and multi-signature sniper transactions, to evade detection.
• Despite limitations, our one-hop funding filter can still capture the clearest, repeatable large-scale "insider" behavior cases.
• This report proposes an actionable heuristic approach to help protocol teams and front-end developers identify, label, and respond to such activities in real-time— including tracking early holding concentration, tagging deployer-associated wallets, and issuing front-end warnings to users during high-risk launches.

Although our analysis only covers a subset of same-block sniping behavior, its scale, structure, and profitability indicate that Solana token launches are being actively manipulated by coordinated networks, while existing defenses are far from sufficient.

Methodology

This analysis begins with a clear objective: to identify behaviors indicating coordinated meme token farming on Solana, particularly the situation where deployers provide funding to sniper wallets in the same block as the token launch. We break the problem down into the following stages:

1. Filtering Same-Block Sniping

We first filter wallets that were sniped in the same block immediately after deployment. Due to the absence of a global mempool on Solana; the need to know the token's address before it appears on public front-ends; and the very short time between deployment and the first DEX interaction, this behavior is almost impossible to occur naturally. Thus, "same-block sniping" becomes a high-confidence filter for identifying potential collusion or privileged activity.

2. Identifying Wallets Associated with Deployers

To distinguish skilled snipers from coordinated "insiders," we tracked SOL transfers between deployers and snipers before the token launch, only marking wallets that met the following criteria: 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. Associating Sniping with Token Profits

For each sniper wallet, we mapped its trading activity on the sniped token, specifically calculating: the total amount of SOL spent to buy the token; the total amount of SOL received from selling on DEX; and the realized net profit (rather than nominal gains). This allows for precise attribution of profits extracted from deployers during each sniping event.

4. Measuring Scale and Wallet Behavior

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

5. Traces of Machine Activity

To understand how these operations are conducted, we grouped sniper activities by UTC hour. The results show: activity concentrated in specific time windows; a significant drop during UTC late-night hours; indicating that rather than being global and continuous automation, it is more aligned with U.S. cron tasks or manual execution windows.

6. Exit Behavior Analysis

Finally, we studied the behavior of deployer-associated wallets when selling the sniped tokens: measuring the time from the first buy to the final sell (holding duration); counting the number of independent sell transactions used for each wallet's exit. This distinguishes whether wallets choose to liquidate quickly or gradually, and examines the relationship between exit speed and profitability.

Focusing on the Clearest Threats

We first measured the scale of same-block sniping during the pump.fun launch, and the results were shocking: over 50% of tokens were sniped in the creation block—same-block sniping has shifted from a fringe case to a dominant issuance model.

On Solana, same-block participation typically requires: pre-signed transactions; off-chain coordination; or shared infrastructure between deployers and buyers.

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

To reduce false positives and highlight genuine coordinated behavior, we incorporated strict filtering in the final metrics: only counting snipes where there was a direct SOL transfer between deployers and sniper wallets before the launch. This allows us to confidently identify: wallets directly controlled by deployers; wallets acting under the deployer's direction; and wallets with insider channels.

Case Study 1: Direct Funding

The deployer wallet 8qUXz3xyx7dtctmjQnXZDWKsWPWSfFnnfwhVtK2jsELE sent a total of 1.2 SOL to three different wallets, then deployed a token named SOL > BNB. The three funded wallets completed their purchases in the same block as the token creation, sniping before broader market visibility. They then quickly sold for profit, executing a coordinated flash exit. This is a textbook example of pre-funded sniper wallets farming tokens, captured directly by our funding chain method. Despite its simplicity, it has been played out on a large scale across thousands of launches.

Case Study 2: Multi-Hop Funding

Wallet GQZLghNrW9NjmJf8gy8iQ4xTJFW4ugqNpH3rJTdqY5kA is associated with multiple token snipes. This entity did not directly fund the sniper wallets but transferred SOL through 5-7 layers of intermediary wallets to the final sniper wallet, completing the sniping in the same block.

Our existing methods only detected some initial transfers from the deployer but failed to capture the entire chain to the final sniper wallet. These relay wallets are often "single-use," only used to pass SOL, making it difficult to associate them through simple queries. This gap is not a design flaw but a trade-off in computational resources—tracking multi-hop funding paths in large-scale data is feasible but resource-intensive. Therefore, the current implementation prioritizes high-confidence, direct linkages to maintain clarity and reproducibility.

We utilized Arkham's visualization tools to showcase this longer funding chain, graphically illustrating how funds flow from the initial wallet through shell wallets to the final deployer wallet. This highlights the complexity of funding source obfuscation and points to future improvements in detection methods.

Why Focus on "Directly Funded and Same-Block Sniping Wallets"

In the remainder of this article, we will only study sniper wallets that directly received deployer funding before the launch and sniped in the same block. The reasons are as follows: they contribute significant profits; have minimal obfuscation techniques; represent the most actionable malicious subset; and studying them can provide the clearest heuristic framework for detecting and mitigating more advanced extraction strategies.

Findings

Focusing on the subset of "same-block sniping + direct funding chain," we reveal a widespread, structured, and highly profitable on-chain coordinated behavior. All data covers the period from March 15 to the present:

1. Same-block and Deployer-Funded Sniping is Very Common and Systematic

a. Over the past month, more than 15,000 tokens were confirmed to be sniped by directly funded wallets in the launch block;

b. Involving over 4,600 sniper wallets and more than 10,400 deployers;

c. Accounting for approximately 1.75% of pump.fun issuance.

2. This Behavior is Highly Profitable

a. Directly funded sniper wallets have realized net profits of over 15,000 SOL;

b. Sniping success rate is 87%, with very few failed transactions;

c. Typical profits per wallet range from 1 to 100 SOL, with a few exceeding 500 SOL.

3. Repeated Deployments and Sniping Indicate a Farming Network

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

b. Some sniper wallets execute hundreds of snipes in a single day;

c. A "hub-and-spoke" structure is observed: one wallet funds multiple sniper wallets, all sniping the same token.

4. Sniping Exhibits a Human-Centric Time Pattern

a. Activity peaks between UTC 14:00 and 23:00; nearly halts between UTC 00:00 and 08:00;

b. Aligning with U.S. working hours indicates manual/cron timed triggers rather than global 24-hour automation.

5. Single-Use Wallets and Multi-Signature Transactions Obfuscate Ownership

a. Deployers fund several wallets simultaneously and sign snipes in the same transaction;

b. These burner wallets do not sign any further transactions afterward;

c. Deployers split initial buys across 2-4 wallets to disguise real demand.

Exit Behavior

To gain deeper insights into how these wallets exit, we break down the data along two major behavioral dimensions:

1. Exit Speed — the time from the first buy to the final sell;

2. Sell Count — the number of independent sell transactions used for the exit.

Data Conclusions

1. Exit Speed

a. 55% of snipes are fully sold within 1 minute;

b. 85% are liquidated within 5 minutes;

c. 11% are completed within 15 seconds.

2. Sell Count

a. Over 90% of sniper wallets exit using only 1-2 sell orders;

b. Very few adopt gradual selling strategies.

3. Profitability Trends

a. The most profitable wallets exit in less than 1 minute, followed by those under 5 minutes;

b. Longer holding periods or multiple sells yield slightly higher average profits per transaction, but the quantities are very small, contributing limited total profits.

Explanation

These patterns indicate that deployer-funded sniping is not a trading behavior but an automated, low-risk extraction strategy:

· Early buy → Quick sell → Complete exit.

· Single sell transactions indicate indifference to price fluctuations, merely exploiting the opportunity to dump.

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

Actionable Insights

The following recommendations aim to help protocol teams, front-end developers, and researchers identify and respond to extraction or coordinated token issuance patterns by transforming observed behaviors into heuristics, filters, and alerts, enhancing user transparency and reducing risk.

Conclusion

This report reveals a persistent, structured, and highly profitable extraction strategy in Solana token issuance: deployer-funded same-block sniping. By tracking direct SOL transfers from deployers to sniper wallets, we identify a set of insider-style behaviors that leverage Solana's high throughput architecture for coordinated extraction.

Although this method only captures a portion of same-block sniping, its scale and patterns indicate that this is not sporadic speculation but rather operators with privileged positions, repeatable systems, and clear intentions. The significance of this strategy lies in:

1. Distorting early market signals, making tokens appear more attractive or competitive;
2. Endangering retail investors—who unknowingly become exit liquidity;
3. Undermining trust in open token issuance, especially on platforms like pump.fun that prioritize speed and ease of use.

Addressing this issue requires not just passive defenses but also better heuristics, front-end alerts, protocol-level safeguards, and ongoing efforts to map and monitor coordinated behaviors. Detection tools already exist—the question is whether the ecosystem is willing to genuinely apply them.

This report takes the first step: providing a reliable, reproducible filter to identify the most obvious coordinated 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.

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