Analysis of Internal Sniping Arbitrage Phenomenon in Solana Token Issuance

This report explores a common and highly coordinated token farming model on Solana: token deployers transfer SOL to "sniping wallets," enabling these wallets to purchase tokens within the same block when the tokens go live. By focusing on the clear financial chain between deployers and snipers, we identified a set of high-confidence extraction behaviors.

Analysis shows that this strategy is neither a coincidence nor marginal behavior. In just the past month, over 15,000 SOL of realized profits were extracted from more than 15,000 token issuances, involving over 4,600 sniper wallets and more than 10,400 deployers. These wallets exhibited an unusually high success rate of (87% profit ), clean exit strategies, and a structured operating model.

Main Findings:

• The deployer's funded sniper systems are systematic, profitable, and usually automated, with activities most concentrated during U.S. working hours.
• The use of multiple wallets to brush farming structures is common, often using temporary wallets and collaborative withdrawals to simulate real demand.
• The means of obfuscation are constantly escalating, such as multi-hop funding chains and multi-signature sniper transactions, to evade detection.
• This analysis presents an actionable heuristic approach to help protocol teams and front-end developers to identify, label, and respond to such activities in real time.

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

Methodology

This analysis aims to identify behaviors indicating collaborative token farming on Solana, particularly in cases where deployers provide funds to sniper wallets at the same block as the token's launch. We divide the issues into the following phases:

1. Filter for intra-block sniping
2. Identify the wallet associated with the deployer
3. Associate sniping with Token profits
4. Measuring Scale and Wallet Behavior
5. Machine Activity Trace
6. Exit Behavior Analysis

Focus on the clearest threats

We first measured the scale of token issuance platform's same-block sniping, and the results were shocking: over 50% of tokens were sniped at the moment of block creation - same-block sniping has shifted from a fringe case to a dominant issuance model.

To reduce false positives and highlight true collusive behavior, we have incorporated strict filtering in the final metrics: only counting direct SOL transfers between deployers and sniping wallets prior to the launch. This allows us to confidently identify: wallets directly controlled by the deployer; wallets acting under the deployer's command; wallets with internal channels.

Case Study 1: Direct Funding

The deployer's wallet sent a total of 1.2 SOL to 3 different wallets, then deployed the token. The 3 funded wallets completed the purchase in the same block as the token creation, preempting visibility in the broader market. Subsequently, they quickly sold for profit, executing a coordinated flash exit. This is a typical example of pre-funded sniper wallets farming tokens.

Case Study 2: Multi-Hop Funding

A certain wallet is related to multiple token sniping activities. This entity did not directly fund the sniping wallet but instead transferred SOL through 5-7 intermediary wallets to the final sniping wallet, thereby completing the sniping within the same block.

Our current approach 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 "single-use", used only to transmit SOL, making it difficult to associate them through simple queries.

found

Focusing on the subset of "same zone sniping + direct capital chain", we reveal a broad, structured, and highly profitable on-chain collaborative behavior. The following data covers from March 15 to the present:

1. Sniping with the same block and funded by the deployer is very common and systematic. a. Over the past month, more than 15,000 tokens have been directly hunted by funding wallets immediately after being launched on the blockchain. b. Involving over 4,600 sniper wallets and over 10,400 deployers c. Accounts for approximately 1.75% of the issuance volume of a certain token issuance platform.

2. This behavior generates large-scale profits a. Direct funding sniper wallet has achieved net profit >15,000 SOL b. Sniping success rate 87%, very few failed trades c. Typical single wallet returns 1-100 SOL, a few exceed 500 SOL

3. Repeated deployment and sniping point brushing farm network a. Many deployers use new wallets to create dozens to hundreds of Tokens in bulk. b. Some sniper wallets execute hundreds of snipes within a day. c. Observed the "center-radiation" structure: one wallet funds multiple sniper wallets, all targeting the same Token.

4. Sniping presents a human-centered time pattern a. The active peak is between UTC 14:00-23:00; almost at a standstill from UTC 00:00-08:00. b. Aligning with US working hours, indicating manual/timed triggers rather than fully automated 24 hours globally.

5. Confusion of ownership between one-time wallets and multi-signature transactions a. The deployer injects funds into several wallets simultaneously and signs the sniper in the same transaction. b. These temporary wallets will not sign any transactions thereafter. c. The deployer splits the initial purchase into 2-4 wallets to disguise the 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 speed - The time from the initial purchase to the final sale
2. Number of sell transactions - The number of independent sell transactions used to exit

Data Conclusion

1. Exit Speed a. 55% of the sniper was sold out within 1 minute b. 85% liquidated within 5 minutes c. 11% completed in 15 seconds

2. Number of sales a. Over 90% of sniper wallets only exit with 1-2 sell orders. b. Rarely adopt progressive selling

3. Profit Trend a. The most profitable is the <1-minute exit wallet, followed by <5 minutes b. Holding for a longer period or selling multiple times may result in a slightly higher average profit per transaction, but the quantity is very small, contributing limited to the total profit.

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-off represents indifference to price fluctuations, merely taking advantage of the opportunity to sell. A few more complex exit strategies are merely exceptions, non-mainstream models.

Conclusion

This report reveals a continuous, structured, and highly profitable Solana token issuance arbitrage strategy: deployer-funded block sniping. By tracking the direct SOL transfers from deployers to the sniping wallets, we identify a batch of insider-style behavior, leveraging Solana's high throughput architecture for collaborative arbitrage.

Although this method only captures a part of the block sniping within the same area, its scale and pattern indicate that this is not scattered 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 Token appear more attractive or competitive.
2. Endanger retail investors - they become exit liquidity without knowing it.
3. Weaken the trust in open token issuance, especially in token issuance platforms that pursue 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 guardrails, and ongoing efforts to map and monitor collaborative behaviors. Detection tools already exist - the problem lies in 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 co-movement behaviors. But this is just the beginning. The real challenge lies in detecting highly obfuscated and constantly evolving strategies, and building an on-chain culture that rewards transparency rather than extraction.