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@un_data_wizSandwiches on Solana
Flipside AI
Solana Sandwich Attack Landscape Analysis
Key Findings
Total Sandwich Attacks Detected: 1,813 on the Solana network, revealing a complex MEV (Miner Extractable Value) exploitation ecosystem.
Attack Characteristics
The sandwich attack landscape demonstrates sophisticated trading strategies with notable insights:
Unique Searchers: 11 distinct sandwich attack searchers were identified, indicating a competitive and strategic MEV extraction environment.
Profit Dynamics
Sandwich trading reveals significant financial volatility:
Daily Profit Highlights:
- Cumulative Profit: $29,063 in a single day
- Dramatic Profit Spike: Gross profit jumped from $182.95 to $2,171.56 within a short timeframe
Risk and Vulnerability
The analysis exposes critical network vulnerabilities:
Validator Risks: Extreme variance exists in sandwich attack vulnerability among Solana validators, suggesting uneven network protection.
Slippage Patterns
Most sandwich attacks generate minimal profits, with probability density concentrated at extremely low slippage percentages.
Strategic Implications
Sandwich attacks represent a sophisticated front-running technique where traders manipulate transaction order to extract profits, highlighting the ongoing cat-and-mouse game in blockchain transaction optimization.
Solana Sandwich Attack Landscape Analysis
Key Findings
Total Sandwich Attacks Detected: 1,813 on the Solana network, revealing a complex MEV (Miner Extractable Value) exploitation ecosystem.
Attack Characteristics
The sandwich attack landscape demonstrates sophisticated trading strategies with notable insights:
Unique Searchers: 11 distinct sandwich attack searchers were identified, indicating a competitive and strategic MEV extraction environment.
Profit Dynamics
Sandwich trading reveals significant financial volatility:
Daily Profit Highlights:
- Cumulative Profit: $29,063 in a single day
- Dramatic Profit Spike: Gross profit jumped from $182.95 to $2,171.56 within a short timeframe
Risk and Vulnerability
The analysis exposes critical network vulnerabilities:
Validator Risks: Extreme variance exists in sandwich attack vulnerability among Solana validators, suggesting uneven network protection.
Slippage Patterns
Most sandwich attacks generate minimal profits, with probability density concentrated at extremely low slippage percentages.
Strategic Implications
Sandwich attacks represent a sophisticated front-running technique where traders manipulate transaction order to extract profits, highlighting the ongoing cat-and-mouse game in blockchain transaction optimization.
When Alice wants to buy a token X on a DEX, other than the amount to swap, she needs to set a slippage. Slippage refers to the difference between the expected price of a trade and the actual price at which it executes, occurring due to market volatility or low liquidity.
At this stage, if Bob sees Alice’s transaction, he can create two of its own transactions which it inserts before and after Alice’s transaction (sandwiching it). In this configuration, Bob buys the same token X, which pushes up the price for Alice’s transaction, and then the third transaction is the adversary’s transaction to sell token X (now at a higher price) at a profit.
n_days: Lookback period. If set to 0, start_date and end_date are used.
start_date: Data collection start date.
end_date: Data collection end date.
granularity: It corresponds to the granularity of the daily PnL plot. If the date range is longer than 24h, set granularity to "day"