Key Insights

On Solana, MEV operates differently than other blockchain networks, primarily due to its unique architecture and lack of a global memory pool. Off-protocol mempools must be developed independently and require adoption by a majority of stake holders in the network to operate effectively, which poses high technical and social barriers.

Jito discontinued its public memory pool service in March 2024, resulting in a significant drop in revenue. This move reduced harmful MEV behavior, but also led to the rise of alternative memory pools that lacked transparency and primarily benefited specific groups.

Memecoin traders are particularly vulnerable to sandwich attacks because they set a high slippage tolerance when trading illiquid and highly volatile assets. This type of user is more likely to use Telegram trading robots to pursue faster transaction execution speed and instant notification services, and is relatively insensitive to transactions being preempted to be executed.

Marinade Finance's pledge auction market (SAM) adopts a competitive auction mechanism, and validators directly bid against each other for pledge allocation through the "pledge to pay" system . However, this mechanism has been controversial because it allows validators making sandwich transactions to bid high to obtain more collateral, thereby increasing their influence in the network.

Most of the sandwich transaction behavior on Solana originates from a private mempool operated by a single entity, DeezNode. A key validator operated by DeezNode (the address starting with HM5 H6) currently holds a delegated stake of 811,604.73 SOL, worth approximately $168.5 million, and has experienced significant growth over the past few months.

Multiple Solana validator operators reported receiving lucrative offers to participate in the private Mempool, including detailed documents outlining profit shares and expected earnings.

Jito bundles are the primary way for searchers to ensure that deal ranking is profitable. However, the Jito data does not cover the full scope of MEV activity; in particular, it does not capture searcher profits or activity through alternative memory pools. Additionally, many applications use Jito for non-MEV purposes, such as bypassing priority fees to ensure timely inclusion of transactions.

In the past year, Jito has processed more than 3 billion transaction bundles, generating a total of 3.75 million SOL tips. This activity has shown a clear upward trend, growing from a tipping low of 781 SOL on January 11, 2024, to 60,801 SOL on November 19.

Jito's arbitrage detection algorithm analyzed all Solana trades, including those outside the Jito bundle, identifying more than 90.44 million successful ones in the past year Arbitrage trade. The average profit per arbitrage trade was $1.58, and while the single most profitable arbitrage trade generated a gain of $3.7 million, these arbitrage trades generated a total profit of $142.8 million.

DeezNode runs a sandwich trading bot on the address starting with vpeNAL. Jito's internal analysis shows that almost half of the sandwich attacks against Solana can be attributed to this program. During a 30-day period (December 7 to January 5), the program executed 1.55 million sandwich transactions and made a profit of 65,880 SOL (approximately $13.43 million). The average profit per sandwich transaction was 0.0425 SOL ($8.67). On an annualized basis, the program will generate profits of over 801,500 SOL per year.

Whitelisting is widely seen as a last resort to combat bad actors, but it could create a semi-permissioned and censorship environment that is at odds with the blockchain industry The idea of ​​decentralization is in direct conflict. In some cases, this approach may also delay transaction processing, impacting the user experience.

Multiple Concurrent Leaders (MCL) systems address harmful MEV issues by allowing users to choose between leaders without incurring delays offers a promising long-term solution. If leader A acts maliciously, users can redirect their transactions to leader B, who is honest. However, it is expected that implementing MCL will require several years of development time.

Maximum Extractable Value (MEV) refers to the value that can be extracted by manipulating the ordering of transactions. This includes adding, deleting, or reordering transactions within a block. Although the various manifestations of MEV are different, they all have one thing in common: they all rely on transaction ordering. Searcher (monitoring activities on the chain)A dynamic trader) who attempts to strategically place his trades before or after other trades to capture value.

On Solana, MEV operates differently and uniquely from other blockchain networks, mainly due to its unique architecture and lack of global memory The condition of the pool. Features such as Turbine (for propagating status updates) and stake-weighted quality of service (SWQoS) for transaction forwarding, together shape Solana's approach to MEV. Its characteristics of fast streaming block production do not require reliance on external plug-ins or out-of-protocol auction mechanisms, which to a certain extent limits the application scope of traditional methods on certain types of MEV (such as front-running). To gain a competitive advantage, searchers run their own nodes or partner with high-stake validators to gain real-time access to the latest state of the blockchain.

Today, the term MEV is overused, and opinions vary on its exact definition. In fact, not all MEVs have negative effects. Due to the distributed and transparent nature of blockchain, it is widely believed in the industry that it is almost impossible to completely eliminate MEV. Networks that claim to have eradicated MEV either lack enough user activity to attract searchers or employ techniques such as random block packing that, while seemingly mitigating the impact of MEV, can also trigger a surge in spam.

Among them, "Sandwiching" is one of the MEV types that has attracted the most attention, and it is extremely detrimental to users. In this strategy, the searcher inserts a trade before and after the target trade in order to profit from it. Sandwich trading is naturally profitable for searchers, but it increases transaction costs and lowers trade execution prices for ordinary users. A detailed discussion of this MEV type will follow.

Above: A visualization of a typical sandwich attack. The attacker conducts front-running and trailing trades before and after the victim's buy transaction to make a profit.

In this report, we will analyze Solana's current MEV landscape, which is divided into four parts:

Solana MEV Timeline: Outlines a series of key events in chronological order, for readers who are not familiar with the rapid development of MEV on Solana.Provide valuable background information to authors;

Types of MEV: Explore the various MEV types currently observed on Solana with specific and detailed examples;< /p>

Solana MEV Data: Provides relevant, quantifiable, and contextual data to illustrate the current scope and impact of MEV in Solana;

MEV Mitigation Mechanisms: Research is considering strategies and mechanisms for reducing or eliminating harmful forms of MEV.

Solana MEV Timeline

The following is a timeline of important events related to Solana MEV.

September 2021 to April 2022: Spam and DDoS attacks

NFT was the first area to gain significant attention on Solana. MEV in the NFT space primarily occurs during public events where participants compete to acquire rare or valuable assets. There is no doubt that these activities create sudden profit opportunities for searchers, with no MEV potential in blocks minted before this and huge MEV potential in blocks minted subsequently. The NFT minting mechanism was one of the earliest causes of massive congestion spikes on Solana caused by spammy transactions sent by bots, which overwhelmed the network and caused block production to be temporarily halted.

Mid-2022: Introducing Priority Fees

Solana has implemented an optional Priority Fees that users can specify in the Calculate Budget command to Prioritize its transactions. This mechanism enhances the network's economic model by enabling users to pay for accelerated processing during peak activity periods to alleviate network congestion and establishes a more efficient framework for the fee market.

In addition, priority fees help curb spam by changing the playing field. Bots that previously relied on brute volume to gain an advantage are no longer able to dominate through spam alone. Instead, priority also depends on what the user is willing to pay.

August 2022: Jito-Solana client goes online

Jito has become the default Solana MEV infrastructure. This client is designed to enable civilian MEV capture.Maintenance ensures a fairer distribution of rewards across the network. When leaders use Jito client validators, their transactions are initially directed to Jito relayers, which act as transaction proxy routers. The relay holds transactions for 200 milliseconds before forwarding them to the leader. This speed buffer delays incoming transaction messages, providing a window for off-chain auctions via the Jito blockchain engine. Searchers and applications submit atomically executed transaction bundles with SOL-based tips. Jito charges 5% on all tips, with a minimum tip of 10,000 lamports. (Bundles can be inspected through the Jito bundle browser.)

Up Figure: The current Jito architecture consists of a block engine that accepts bundles submitted by searchers and a relay that delays incoming transactions to the leader

This approach reduces spam and increases the efficiency of Solana's computing resources by conducting the auction off-chain and publishing only a single winner into a block. This is especially important considering that unsuccessful transactions consume a significant portion of the network's computing resources.

In its first nine months, Jito-Solana client adoption has been below 10% as network activity remains low and MEV rewards are few . Starting in late 2023, adoption accelerated significantly, reaching 50% by January 2024. Today, over 92% of Solana validators (weighted by stake) use the Jito-Solana client.

January 2024: Memecoin Season Begins

Early 2024 sees a surge in network activity. Memecoins such as Bonk and DogWifHat gained popularity, sparking high interest from searchers and resulting in a significant increase in MEV activity. This period marked a significant shift in user behavior: Memecoin traders preferred Telegram trading bots such as BonkBot, Trojan, and Photon to traditional decentralized exchanges or aggregators. These bots offer faster trading speeds, real-time notifications, and an intuitive user interface that appeals to retail speculators. It is worth noting that these traders are often willing to set higher slippage rates to ensure that time-sensitive trades canPrioritize processing, and are relatively less concerned about their own transactions being preempted.

March 2024: Jito suspends its flagship Mempool feature

Jito's Mempool provides searchers with a 200 millisecond window to preview all incoming A leader's deal. During its operation, the system was often used for Sandwich Attacks, which seriously degraded the user experience. In order to prioritize the long-term growth and stability of the network, Jito made the controversial decision to suspend its Mempool, sacrificing significant revenue in the process. While the move received widespread support, it was also criticized by several high-profile figures, including Mert Mumtaz and Jon Charbonneau.

The main risk of this decision is that alternative memory pools may emerge that replicate Jito's functionality, allowing more harmful forms of MEV to emerge. Because unlike public mempools, which facilitate a fairer distribution of MEV opportunities and alleviate power imbalances within the network, private permissioned mempools lack transparency and benefit only a select few with access.

Above: Part of the DeezNode MEV proposal regarding "DeezMempool" . Shortly after Jito Mempool was suspended, multiple validators reported receiving DeezNode MEV proposals.

Multiple Solana validator operators reported receiving lucrative offers to participate in a private Mempool.

May 2024: New transaction scheduler

As part of the Agave-Solana 1.18 update, a new scheduler (Scheduler) significantly improves Solana’s determinism The ability to order transactions in a manner. The improved scheduler can better prioritize transactions with higher fees to increase their likelihood of being included in a block. The central scheduler builds a dependency graph called a "prio-graph" to optimize the processing and prioritization of conflicting transactions across multiple threads.

Previously, bots engaged in arbitrage and other MEV activities were incentivized to increase successful execution by spamming leadersopportunity. The randomness of the old scheduler resulted in variability in transaction positions within blocks. However, new deterministic methods reduce this randomness, suppressing spam and improving the overall efficiency of the network.

June 2024: Marinade launches the Staking Auction Market (SAM)

Marinade Finance’s Staking Auction Market (SAM) uses a bidding auction mechanism in which the verifier Bid directly against each other for pledge allocation through the "Stake to Pay" system. This structure incentivizes validators to bid to the highest rate they believe is profitable. However, this mechanism has been controversial because it allows validators making sandwich transactions to bid high to obtain more collateral, thereby increasing their influence in the network. Marinade Labs recently proposed the creation of a public committee to oversee delegation practices. After Jito, Marinade Finance’s mSOL has become the second largest liquid staking token and staking pool on Solana.

Above: The pledge bidding situation in the Marinade Finance pledge auction market ( December 27, 2024)

As of epoch 717, the staking commission is 0% and the MEV commission is 0% Validators typically offer stakers around 9.4% APY (annualized yield). Validators that use off-protocol methods to redistribute block rewards typically offer 10% APY or less. By comparison, Marinade’s SAM auction showed a winning APY of 13.73%, with the top ten validators bidding a whopping 18.27% APY.

This discrepancy suggests that these validators are either bidding unreasonably and suffering losses as a result (they may be subsidizing their bids through staking delegation from the Solana Foundation), or Supplement revenue through other sources such as MEV extracted from user sandwich transactions.

December 2024: Concerns about new private mempools grow

Solana MEV becomes the posed a controversial topic, sparked widespread discussion, and once again prompted the ecological team to address the challenges posed by Solana MEV.

Validators who perform harmful MEV withdrawals will receive a disproportionate amount of value for their contributions, causing their stake to grow much faster than other validators. This allows validators to accumulate greater network influence over time, thereby introducing centralization risks to Solana’s validator economy. These higher-yield validators can also offer higher rewards to stakers, thereby attracting more pledges and further expanding their advantageous position.

It should be noted that most of Solana's sandwich transaction behavior originates from a private memory pool operated by a single entity, DeezNode. A key validator (address starting with HM5H6) operated by DeezNode currently holds a delegated stake of 811,604.73 SOL, worth approximately $168.5 million. The validator's delegated staking has experienced significant growth, from 307,900 SOL on November 13 (the 697th epoch) to 802,500 SOL on December 9 (the 709th epoch). Since then, the growth has stabilized. . It is worth mentioning that 19.89% of the pledges come from Marinade’s mSOL liquid pledge pool and Marinade’s native delegation. This validator currently holds 0.2% of total staked volume (currently 392.5 million SOL), ranking 93rd by staked volume within the broader set of validators.

Jito's internal analysis shows that an increasing number of sandwich attacks occur outside of Jito's auction mechanism, indicating the existence of additional block engines or modifications of validator clients conducting such transactions.

Types of MEV

Next, let’s take a look at the various MEV types on Solana and illustrate each type with concrete examples of actual transactions. Below are the most common MEV transaction types currently observed on Solana.

Liquidation

When a borrower fails to maintain the collateral ratio required for its loan under the lending agreement, its position becomes eligible for liquidation. Seekers monitor these undercollateralized positions on the blockchain and perform liquidations by paying off some or all of the debt in exchange for a portion of the collateral as a reward. Liquidation is considered a benign type of MEV. They are critical to maintaining protocol solvency and promoting the stability of the broader DeFi ecosystem.

Liquidation transaction example

This liquidation event occurred on December 10 and involved Kamino, the largest lending protocol on Solana by liquidity and user base. The transaction Contains three steps:

The searcher initiates liquidation by transferring 10.642 USDC to the Kamino reserve to cover the user's debt position.

In exchange, Kamino Reserve transfers the user's 0.05479 SOL collateral to the searcher.

The searcher paid a protocol fee of 0.0013 SOL.

In addition, the searcher also paid a priority fee of 0.001317 SOL for this transaction, thereby obtaining Net profit of $0.0492

Above: Solana’s Kamino Money Market. Examples of clearing transactions on

Arbitrage

Arbitrage uses the price difference of the same asset to improve market efficiency and make profits by adjusting prices in different trading venues. These opportunities may occur within the chain, cross-chain, or between centralized exchanges and decentralized exchanges (CEX /DEX arbitrage). Among them, intra-chain arbitrage guarantees atomicity because the two parts of the transaction can be executed together in a single Solana transaction. In contrast, cross-chain and cross-platform arbitrage introduce additional trust assumptions.

Atomic arbitrage is the primary form of MEV on Solana. The simplest example of atomic arbitrage occurs when two DEXs list different prices for the same trading pair. This usually involves automated market making utilizing a constant product model (xy=k) Outdated price information on the market maker (AMM), and hedging transactions are performed on the chain's price limit order book. At this time, the market maker has adjusted the quotation based on the price changes off the chain.

Arbitrage trade example

Above: Arbitrage between two decentralized exchangesTransaction Example

In this case, the price of the SOL/USDC trading pair has changed off-chain, prompting Phoenix market makers to respond accordingly Update their offer. Meanwhile, Orca AMM still bases its quotes on outdated prices, creating arbitrage opportunities for searchers. The searcher purchased 2.11513 SOL on Orca for 45 USDC and then sold 2.115 SOL on Phoenix for 45.0045 USDC, making a profit of 0.00013 SOL (approximately $0.026 USD). Arbitrage trades are executed atomically and do not require the searcher to hold inventory. The only risk is that there may be fees if the transaction is reversed.

Front Running

Front Running means that the MEV searcher identifies another trader's buy or sell order in the memory pool and executes it in the memory pool. The trader had previously placed the same order, profiting from the price impact of the victim's trade.

Occurs when an observer notices that an unconfirmed transaction may affect the token price and acts based on this information before the original transaction is processed Be the first to trade. This strategy is simple and straightforward and does not involve the complexity of other methods such as sandwich attacks.

The searcher realizes that there is a pending buy transaction that will have a positive impact on the price of the target token, so it bundles its buy transaction with the target transaction together. Their order will be processed at a price below the target, and they will make a profit once the target trade is completed. In the process, the target trader suffers losses as a result of the buy trades of MEV searchers who buy at a higher price.

Back Running

Back Running is the counterpart of front-running trading. It is a specific MEV strategy that uses the temporary price imbalance caused by another transaction to gain profits. advantage, and this imbalance is usually caused by improper routing. Once a user's trade is executed, trailing trade seekers balance prices across pools by trading the same asset and ensure a profit. In theory, users could have captured this profit through more efficient trade execution.

Example of tailing transaction

This famous tailing transaction occurred on January 10, 2024 day, when aUsers purchased $8.9 million worth of DogWifHat token WIF in one transaction. At the time, the WIF token was trading at $0.20, and liquidity across all on-chain trading venues combined was only a few million dollars. The Jupiter aggregator executed the trade through three pools with limited liquidity, causing the price to surge to $3.

The searcher performed a trailing trade using the Jito Bundle and provided a generous Jito tip of up to 890.42 SOL ($91,621). They first exchanged 703.31 SOL ($72,368) for 490,143.90 WIF tokens through a Raydium centralized liquidity pool. They then exchanged these WIF tokens for 19035.97 SOL ($1,958,733) via the Raydium V4 liquidity pool. This series of operations resulted in a net profit of 17442.24 SOL ($1,794,746) in a single transaction. All dollar values ​​reflect prices at the time of transaction.

< em>Above: Trailing transactions after a large purchase of WIF tokens in January 2024

Sandwich Attacks

Sandwich Attacks are MEV One of the most destructive types of trading specifically targets traders who set high slippage tolerances on Automated Market Makers (AMMs) or Bonding Curves. These traders increase their slippage tolerance not to accept worse prices, but to ensure that orders are executed quickly. Among them, Memecoin traders are particularly vulnerable to sandwich attacks because they tend to set a high slippage tolerance when trading illiquid and highly volatile assets, which ultimately leads them to trade at extremely unfavorable prices.

A typical sandwich attack involves three transactions that are atomically bundled together. First, the attacker executes an unprofitable front-running trade, purchasing an asset to drive its price to the worst execution level allowed by the victim's slippage settings. Next, the victim's trade occurs, and since it is executed at an unfavorable price level, the price increases further. Finally, the attacker completes a profitable trailing transaction, selling the asset at an inflated price to offset theeliminate its initial losses and obtain a net profit.

Sandwich attack transaction example

This attack occurred on December 16, 2024. Via a well-known sandwich attack program (vpeNALD…Noax38b). Searchers submitted these transactions as atomic Jito bundles and were tipped 0.000148 SOL (approximately $0.03 USD).

Preemptive transaction: The searcher paid 14.63 SOL to purchase 32.9 million Komeko tokens, which is a newly launched Memecoin on the Pump Fun platform;

p>

Victim transaction: purchased 624,000 Komeko tokens with 0.33 SOL;

Tail trade: Seeker sells 32.9 million Komeko tokens for 14.65 SOL.

< em>Above: An example of a sandwich attack that bundles three transactions

Characteristics indicating that this is a sandwich attack:

The signer of the intermediate transaction is different from the signer of the first and last transactions.

The tokens purchased in the first two transactions are the same tokens sold in the third transaction.

The token being traded is a newly minted, illiquid and highly volatile Pump Fun token.

The searcher made a net profit of 0.01678 SOL, equivalent to approximately $3.35 at the time of the transaction.

Solana MEV Data

This section evaluates the current MEV profile of Solana using existing public data. First analyze Jito's performance metrics, and then drill down into the number of Reverted Transactions and a breakdown of arbitrage profitability. It ends with a case study detailing the behavior and profitability of a well-known sandwich trading robot.

Jito

Jito bundles are the primary way for searchers to ensure profitable ordering of deals. Most Jito tips come from demand for block tops from users looking to be among the first to buy the token or take advantage of the opportunity. However, the Jito data does not cover the full scope of MEV activity; in particular, it does not capture searcher profits or activity through alternative memory pools. Additionally, many applications use Jito for non-MEV purposes, bypassing priority fees to ensure timely inclusion of transactions.

Data from transfers to eight designated Jito tip accounts shows that over the past year, Jito has processed more than 3 billion transaction bundles, totaling Tip of 3.75 million SOL. This activity has shown a clear upward trend, from a tip low of 781 SOL on January 11, 2024, to tips as high as 60,801 SOL and 60,636 SOL on November 19 and 20, respectively. Looking at the chart, Q3 saw a noticeable slowdown, with tips falling to a low of 1,661 SOL on September 7. Additionally, the value of tips through December 2023 is minuscule compared to the substantial increase through 2024 as a whole.

Above: Jito tip in SOL of daily amounts (data source: Dune Analytics, 21co)

The number of bundles processed through Jito continued to grow throughout 2024, ultimately Reached 2440 on December 21st Peak of 10,000 bundles. This increase included two significant surges. The first surge occurred between May and early July, with the number of daily bundles increasing from about 3 million to 12 million, possibly in response to network congestion issues. The second surge occurred between November and December, with the number of daily bundles doubling from approximately 12 million to a peak of 24 million.

Above: Daily quantity of daily Jito tips (bundles) throughout 2024 (data source: Dune Analytics, Andrew Hong)

During this period, the number of accounts using Jito also showed a parallel upward trend. At the beginning of the year, there were approximately 20,000 tip payers per day, reaching nearly 10,000 on December 10. Peak of 938,000. Notable periods of growth include an increase from 21,000 in early March to 135,000 in mid-April (a 6-fold increase), and a significant increase from 208,000 in October to 703,000 at the end of the month (a 3.4-fold increase).

Above: Jito’s daily number of tip payers (data source: Dune Analytics, Andrew Hong)

Adoption of the Jito-Solana client among validators has grown steadily throughout 2024, enhancing the Jito bundle Effectiveness in fast transaction inclusion. At the beginning of the year, validators using the Jito-Solana client staked 189.5 million SOL, accounting for 48% of the total staked volume on the network. By early 2025, this number had grown to 373.8 million SOL, accounting for 92% of the total pledged volume.

Above: Jito-Solana validator adoption rate in 2024 by Growth in staking calculations (data source: Jito)

Withdrawal of transactions

A large portion of transactions on Solana are spam related to MEV withdrawals related. By examining the ratio of Reverted Transactions to successful transactions, we can identify patterns that indicate MEV bots competing to capture arbitrage opportunities.

Above: Weekly reversals and successful non-voting transactions in 2024 Quantity (data source: Blockworks Research)

Spam poses a huge challenge as it can cause many transactions to be reversed. Under the winner-take-all nature of MEV, only one transaction can exploit a given opportunity. However, even after the opportunity is captured, the leader still handles other transactions trying to exploit the same opportunity. These reversed transactions still consume valuable computing resources and network bandwidth. Competitive latency races among searchers further exacerbate the problem, flooding the network with duplicate transactions and, in extreme cases, leading to congestion and degraded user experience. Due to Solana's low transaction costs, the arbitrage spam that was reversed still has a positive expected value. Over time, traders can achieve profitability by executing these trades on a large scale (although individual trades may fail).

Reversal transactions peaked in April 2024, accounting for 75.7% of all non-voting transactions. That dropped significantly after the rollout of key updates including the Agave 1.18 central scheduler. The new scheduler improves deterministic transaction ordering within the "Banking Stage", thereby suppressing the effectiveness of spam.

Above: Reversed transactions as a percentage of all non-voting transactions (data Source: Dune Analytics, 21co)

Arbitrage Profitability

Jito's arbitrage detection algorithm analyzes all Solana transactions, including those outside the Jito bundle , identified in the past year 90,445,905 successful arbitrage trades. The average profit per arbitrage trade was $1.58, with the single most profitable arbitrage trade generating $3.7 million. These arbitrage trades generated a total profit of $142.8 million, of which $126.7 million (88.7%) was denominated in SOL.

Above: Arbitrage profits by token in 2024 ( Data source: Jito)

Case Study: Vpe Sandwich Trading Program

DeezNode runs a sandwich trading robot on the address starting with vpeNAL as its replacement memory part of the pool operation.This highly active program has recently gained notoriety for executing large-scale user sandwich attacks.

Above: Sandwich transaction bundles initiated by the Vpe sandwich transaction program every hour Packet count (data source: Flipside crypto analytics, Marqu)

Jito's internal analysis shows that almost half of the sandwich attacks against Solana can be attributed this procedure.

Above: Vpe program is the source of nearly half of Solana sandwich attacks ( Source: Jito internal)

During a 30-day period (December 7 to January 5), the program was executed 1.55 million sandwich transactions, an average of approximately 51,600 transactions, 88.9% success rate. The program generated a profit of 65,880 SOL ($13.43 million), which equates to approximately 2,200 SOL earned per day. Jito tips paid by the program totaled 22,760 SOL ($4.63 million), averaging about 758 SOL per day, with an average profit of 0.0425 SOL ($8.67) per sandwich transaction.

Above: Vpe Sandwich Trading Robot from December 7, 2024 Value extracted as of January 5, 2025

Most of the victimized transactions involved exchanges through Raydium. Of the top 20 sandwiched tokens, 16 were created on Pump Fun and can be identified by their token minting addresses ending in “pump.”

The Vpe sandwich trading bot is one of many on-chain programs that performs sandwich attacks. Click to visit Sandwiched.me to view sandwich attacks detected on Solana in real time.

Annualizing December's profit data, the program is expected to generate an annual profit of 801,540 SOL. In the worst-case scenario of network centralization, if all of these profits are reinvested in validators that replace the memory pool , assuming the overall network staking volume remains unchanged, their network staking share will increase by 0.2%

However, this worst-case scenario is unlikely for several reasons. First, the network's current activity levels are near all-time highs; second, it is reasonable to assume that mempool seekers and operators will cash out portion of the profits, rather than reinvesting all proceeds

MEV Mitigation Mechanisms

Significant resources have been devoted to researching and exploring various mitigations or reallocations of MEVs. Mechanisms. Universal, off-protocol solutions are increasingly integrated into applications and infrastructure to minimize the scope of on-chain MEV verification.

These mechanisms are detailed below. One proposal is for stakers, RPC node providers, and other validators to whitelist by omitting the leadership slot of validators found to be conducting sandwich attacks. slots), thus excluding them. However, whitelisting is widely viewed as a last resort. Assuming a leader is assigned four consecutive slots, this approach can delay transaction processing by several seconds, resulting in a poor user experience. What’s more, whitelisting has the potential to create a semi-permissioned and censorship environment, which is in direct conflict with the blockchain industry’s philosophy of decentralization. Additionally, such a system has the inherent risk of incorrectly excluding honest validators. This can erode trust and engagement on the network

It is worth mentioning that some independent developers and applications are free to build their own validator allowed or denied lists, such as the sendTransaction method in the Helius Node.js SDK.

Dynamic slippage and MEV protection

Managing slippage has traditionally been a challenging and cumbersome process for users, requiring them to make manual adjustments based on the token they trade. When dealing with volatile or less liquid tokens, This approach is particularly cumbersome because the slippage settings suitable for stable assets such as liquid collateral tokens or stablecoins are significantly different from those suitable for Memecoins

August 2024, Solana’s latestPopular retail trading platform Jupiter Aggregator introduces Dynamic Slippage to address this complexity. This algorithmic mechanism optimizes slippage settings in real time using a set of heuristics to calculate the ideal slippage threshold for each trade. These heuristics consider factors including:

Current market conditions

Tokens being traded Type (e.g. stablecoin pair vs. volatile Memecoin)

Fund pool or order book through which the transaction passes

User’s maximum slippage tolerance

These heuristics ensure that trades are optimized for success with minimal slippage, thereby reducing the scope for MEV withdrawals.

MEV protection mode is increasingly common in decentralized exchanges and Telegram trading bots. When enabled, user transactions will only be routed to the Jito block engine, significantly reducing the risk of sandwich attacks. However, this protection comes at the cost of slightly higher transaction fees, so even if MEV protection is offered, many Telegram bots will not choose to enable it. Because they focus more on the rapid incorporation of transactions, prioritizing speed over reducing the risk of sandwich attacks.

Request for Quotation System

RFQ (Request for Quote) systems are getting a lot of attention on Solana, they allow professional market makers instead of on-chain automated market makers (AMMs) or order book to fulfill orders. These systems use signature-based pricing, allowing off-chain calculations and the price discovery process to occur off-chain, while only the final transaction is recorded on-chain. Here are some examples:

Kamino Swap: An intent-based trading platform designed to eliminate slippage and MEV. Kamino uses the Pyth Express Relay to broadcast exchange requests to the searcher network, and the searchers bid to complete the transaction. The winning searcher provides the best execution price and tips the user. In the event of an arbitrage opportunity, a searcher may execute a trade at a better price than requested, resulting in a trade "surplus". Users benefit by retaining any surplus from their trades, thereby increasing their overall execution value.

JupiterZ (Jupiter RFQ): Starting in December, all exchanges on Jupiter will have JupiterZ enabled by default. This feature allows exchanges to automatically select the best price between Jupiter's standard on-chain routing engine and the RFQ system. Via RFQ , users benefit from no slippage or MEV because trades are executed directly with off-chain market makers. Additionally, market makers bear transaction priority fees and there is no need for complex routing logic.

RFQ systems work well for tokens that are widely traded on CEX. However, they are less effective for newer, less liquid, and more volatile on-chain assets. Unfortunately, these trades are exactly Transactions most vulnerable to MEV attacks. Another disadvantage is that liquidity is moved off-chain, thus reducing composability. AMM is resistant to sandwich attacks. sandwich attack AMM (sr-AMM) is an experimental design based on the traditional constant product model (xy=k) AMM. Its core is to use geometric formulas to automatically adjust the token price in the fund pool

sr-AMM uses slot windows to manage transactions. Transactions within a slot window have an asymmetric impact on the buy and sell order pool:

When a buy order is executed, the selling price on the pool rises along the xy=k curve, while the buying price remains unchanged, thus effectively increasing the liquidity of the buyer;

< p style="text-align: left;">Conversely, sell orders consume this buy-side liquidity, thereby reducing the quote determined by the xy=k curve

At the beginning of each new slot window, sr-AMM is reset to the equivalent xy=k state, recalibrating bid and ask prices. By decoupling these resets from individual transactions and maintaining consistent pricing within each slot window, sr-AMM breaks the atomic execution required for sandwich attacks. This renders it ineffective.

However, sandwich attacks are still possible at the boundaries between slot windows. If the leader controls consecutive slot windows, they can do so. Front-running and target trades are executed at the end of the first slot window, and then in the next slot A trailing run is performed at the start of the window.

In November of this year, Ellipsis Labs released Plasma, a reference implementation of an audited sandwich attack-resistant AMM design.

Conditional liquidity and order flow segmentation

Decentralized exchanges (DEX) currently lack a mechanism to apply variable pricing to different types of market participants. This limitation stems from the inability of DEX to accurately identify the cost of order flow to the DEX protocol. DEX The spread will be narrowed to attract order flow, but it will inadvertently increase the risk of adverse selection from mature buyers.

Conditional liquidity introduces a conditional liquidity. A new mechanism that allows DEXs to dynamically adjust spreads based on the "expected toxicity" (expected malicious behavior or potential harmful effects) of incoming order flow. This enables DEXs to express a wider range of on-chain preferences on-the-fly. Rather than offering a single spread to all participants, conditional liquidity enables a DEX to present a gradient of spreads that is calibrated based on the perceived likelihood of adverse selection by a given recipient.

This process relies on a new type of market participants, namely "segmenters". Segmenters specialize in assessing the “toxicity” of order flow and adjusting spreads accordingly. They take a portion of the adjusted spread as compensation while passing the remainder to the wallet or trader. By setting accountability for managing spreads, segmenters enable DEXs to better compete for non-toxic order flow. Segments compete with each other to minimize the risk of adverse selection of liquidity providers. The strictest quotes are reserved for traffic deemed least likely to harm liquidity providers. In its simplest form, a wallet or application can act as a segmenter for its own order flow. Alternatively, it could delegate responsibility for traffic segmentation to the market.

Users take advantage of this through "declarative exchanges", which allow them to declare exchanges Intent and leverage segmenters for execution. These exchanges interact with existing Solana liquidity sources and DEXs that enable conditional liquidity. Declarative exchanges built with Jito bundles provide traders with guaranteed quotes at signature time, while recalculating the optimal route before a transaction enters the network, ensuring initial quotes are adhered to.

This approach significantly reduces the delay between route calculation and transaction finalization, thereby mitigating slippage. Additionally, when routed through a DEX with conditional liquidity enabled, declarative swaps minimizePossibility of sandwich attack. These DEXs improve trading conditions for Solana users by providing tighter spreads for non-toxic traffic. As a result, declarative exchanges provide traders with the ability to reduce slippage, reduce latency, and enhance protection against sandwich attacks, resulting in a more efficient and secure trading experience.

Paladin

Paladin-Solana is an improved version of the Jito-Solana validator client, which is bundled by introducing a minimal code patch (about 2000 lines of code) The package phase contains Paladin priority port (P3) transactions. The Paladin priority port (P3) facilitates high-priority fee transactions. Validators act as leaders to open this fast lane, allowing them to process valuable transactions in a timely manner. Every P3 transaction meets the minimum fee threshold (10 lambors per compute unit) and is passed directly to the packaging stage, where it is processed in the order in which it was received.

Paladin prioritizes high-priority fee transactions and proactively identifies and discards sandwich transaction bundles based on transaction patterns. While this may initially seem detrimental to validator rewards, Paladin validators can be compensated through a trust-based mechanism. Validators that avoid sandwich attacks can attract direct transactions, creating an ecosystem of trust and increasing yields.

Validators are incentivized by the prospect of additional rewards and by relying on the trust of P3 Fast Track users. However, if they include sandwich transaction bundles in their blocks, they may lose P3 transaction revenue. This trust is collateralized by PAL tokens.

The PAL token is designed to align the interests of validators, users, and the broader Solana community. It has a fixed supply of 1 billion tokens, 65% of which will be allocated to validators and stakers, and the remainder will be divided between Solana builders, the Paladin team, and a development fund. Validators can enable P3 transactions on their nodes by locking PAL, creating a decentralized, permissionless, and token-controlled mechanism for MEV withdrawal and transaction prioritization.

The project is still in its early stages and has not yet reached critical scale for widespread adoption. Currently, there are 80 validators running Paladin, accounting for 6% of the network’s stake. Paladin claims to increase block rewards by 12.5%.

Multiple concurrent leaders

Block producers maintain a monopoly on transaction inclusion within their assigned slots. Even if the current leader is known to maliciously perform a sandwich attack, users will be unaware, submit transactions and expect them to be processed immediately. Users cannot choose which node processes and orders transactions, making them vulnerable to manipulation.

The Multiple Concurrent Leaders (MCL) system introduces competition among block producers within the same period. Users gain the ability to select a leader without causing latency. If leader A is malicious and known to perform a sandwich attack, the user or application can choose to submit the transaction to leader B, which behaves honestly.

Maximizing competition among leaders in the long term involves shortening the duration, limiting the number of consecutive slots a single leader is assigned, and increasing the number of concurrent leaders per slot number of persons. By dispatching more leaders per second, users gain greater flexibility, allowing them to choose the most advantageous quotes from available leaders for trading.

Although MCL provides a compelling long-term solution to address MEV, its implementation is complex and may require years of development time.

Asynchronous execution (AE) is another potential method to reduce MEV. Under AE, blocks are constructed without executing or evaluating the outcome of each transaction. This speed poses a significant challenge to algorithms in calculating profit opportunities and executing effective sandwich strategies in a timely manner.

Conclusion

Solana's MEV landscape is rapidly evolving and is far from reaching a stable competitive equilibrium. Searchers continue to explore more sophisticated strategies to capture value, while ecosystems deploy diverse infrastructure and mechanisms to mitigate the effects of harmful MEVs. Forward-looking ecosystem investors such as Multicoin Capital are actively deploying funds, believing that the value that ecosystem teams derive from Solana MEV will grow significantly and that the landscape of this value distribution will change significantly in the coming years.

Above: MEV’s value capture distribution (Source: Multicoin Capital, Tushar Jain)

For any blockchain that carries major financial activities, MEV is an inevitable challenge. Properly addressing and managing this "MEV demon" is critical to the long-term success of the network. After emerging from the difficulties of 2023, Solana has definitely emerged stronger and is now a blockchain with high activity and growing user adoption. However, new challenges lie ahead. To achieve wider adoption, the ecosystem must address these challenges head-on. Currently, Solana is at a critical juncture in its development, which presents both challenges and valuable opportunities that will define its future.