Generalized Frontrunner

Unlike a MEV bot focused solely on arbitrage, a generalized frontrunner can execute a wide range of strategies. These include:

• Arbitrage across DEXs and CEXs
• Liquidations in lending protocols
• NFT floor price arbitrage
• Cross-domain opportunities (e.g., bridging assets) It uses a unified framework to evaluate and act on any profitable on-chain state change.

The core mechanism involves simulating the outcome of pending transactions in the mempool. The frontrunner:

1. Scans for high-value user transactions.
2. Simulates the transaction locally to determine its profit potential.
3. Constructs a competing bundle, often outbidding the original transaction on gas.
4. Submits its bundle to a block builder or validator for inclusion. This process is automated and occurs in milliseconds.

Generalized frontrunners do not typically propose blocks themselves. They rely on a separation of roles:

• Searchers (the frontrunners) discover opportunities and create transaction bundles.
• Block Builders aggregate these bundles and others to construct the most profitable block.
• Validators/Proposers select the highest-paying block header. This ecosystem is facilitated by platforms like Flashbots SUAVE and private mempools.

These agents have a dual economic role:

• Positive: They contribute to price efficiency across markets and ensure liquidation solvency for lending protocols.
• Negative: They can impose negative externalities on regular users through gas price inflation and transaction failure (e.g., sandwich attacks). The net impact is a subject of ongoing protocol design and research.

Operating a competitive generalized frontrunner requires sophisticated infrastructure:

• Low-latency node connections to multiple networks.
• High-performance transaction simulation engines (e.g., using EVM trace APIs).
• Access to private transaction pools or builder APIs to submit bundles.
• Sophisticated risk and portfolio management systems to manage capital across strategies.

The future of generalized frontrunning is tied to core protocol upgrades:

• Proposer-Builder Separation (PBS) formalizes the role of specialized builders, creating a market for bundle inclusion.
• SUAVE (Single Unified Auction for Value Expression) is a proposed decentralized mempool and block builder network that aims to democratize access to MEV opportunities, potentially changing how frontrunners operate.

The most common form of generalized frontrunning, where a frontrunner places one transaction before and one after a victim's trade. This manipulates the price to extract value from the victim's slippage.

• Mechanism: Frontrun to buy the asset, victim's trade pushes price up, backrun to sell at a profit.
• Target: Large, visible DEX trades on pools like Uniswap or SushiSwap.
• Result: Victim receives worse execution; frontrunner profits from the spread.

A race to be the first to trigger and profit from an undercollateralized loan liquidation on lending protocols like Aave or Compound.

• Mechanism: Bots monitor for positions nearing the liquidation threshold. They compete to submit the first valid transaction to liquidate, earning a liquidation bonus.
• Target: Specific function calls to liquidate() or liquidateBorrow().
• Key Factor: Success depends entirely on transaction ordering and gas price.

Bots detect profitable arbitrage opportunities (price differences between DEXs) and frontrun other searchers to capture the profit.

• Mechanism: Uses mempool snooping to copy pending arbitrage transactions. The frontrunner submits an identical transaction with a higher gas fee to execute first.
• Target: Cross-DEX swap transactions (e.g., buying ETH on Uniswap to sell on SushiSwap).
• Impact: Reduces profitability for honest arbitrage bots, centralizing MEV.

Exploiting the public mempool to secure limited-edition NFTs before other users during a public mint.

• Mechanism: A bot watches for mint transactions. It replicates the transaction call but replaces the recipient address with its own and submits it with a higher gas fee.
• Target: mint() function calls on popular NFT collection contracts.
• Result: Legitimate users fail to mint, while frontrunners acquire the NFT, often to immediately resell.

A sophisticated attack where a frontrunner anticipates and exploits price updates from oracles like Chainlink.

• Mechanism: The bot sees a pending transaction that will cause a large price update (e.g., a big DEX trade). It frontruns with positions (like loans or options) that profit from the imminent price change before the oracle reports it.
• Target: Protocols relying on delayed price feeds for critical functions like loan health checks.
• Complexity: Requires deep understanding of the target protocol's oracle integration.

A historical attack vector where validators or miners reorganize the blockchain to steal already-included MEV. This is a form of consensus-level frontrunning.

• Mechanism: A validator rewrites recent blocks to exclude a profitable MEV transaction and include their own version instead.
• Target: Any high-value MEV transaction after it has been mined but before the chain is considered final.
• Modern Relevance: Mitigated by faster finality (e.g., Ethereum's single-slot finality) but remains a risk on some chains.

A generalized frontrunner operates by:

• Continuously monitoring the public transaction mempool.
• Identifying pending transactions that create arbitrage, liquidation, or other MEV opportunities.
• Simulating the outcome of inserting a new transaction before the target.
• Submitting a higher-fee transaction to be mined first, front-running the original user. This process is automated and often targets decentralized exchange (DEX) trades and lending protocol liquidations.

Generalized frontrunning can enable time-bandit attacks, where miners or validators are incentivized to reorganize the blockchain (reorg) to capture missed MEV. This undermines blockchain finality and consensus security. If the value of reorg-based MEV exceeds the block reward and penalties, it threatens the network's liveness and safety guarantees, as described in the Goldfinger attack model.

Frontrunners create negative externalities for the network:

• Gas price auctions: They bid up transaction fees to ensure priority, making regular transactions prohibitively expensive during high-MEV periods.
• Network congestion: The flood of competing frontrunning transactions clogs the mempool, increasing latency for all users.
• Wasted computation: Many simulated and submitted transactions fail, consuming block space and validator resources without adding value.

End-users suffer directly from generalized frontrunning:

• Failed transactions: Original trades or actions are often sandwiched or fail due to price slippage caused by the frontrunner.
• Economic loss: Users receive worse prices (slippage) on trades, or have their profitable transactions replaced.
• Unpredictability: Transaction outcomes become less reliable, eroding trust in the system's fairness and predictability.

Key solutions aim to neutralize generalized frontrunning:

• Private Transaction Relays (e.g., Flashbots Protect): Allow users to submit transactions directly to builders/validators, bypassing the public mempool.
• Proposer-Builder Separation (PBS): Separates the role of block building (by competitive builders who can include MEV) from block proposal. This can formalize MEV extraction and reduce the incentive for disruptive chain reorgs.
• Fair Sequencing Services: Protocols that order transactions based on arrival time rather than fee price.

A sandwich attack is a specific, common strategy executed by generalized frontrunners against a DEX trade. The attacker:

1. Front-runs the victim's buy order with their own buy, driving the price up.
2. Lets the victim's order execute at the inflated price.
3. Back-runs the victim by immediately selling the asset, profiting from the artificial price movement. This directly extracts value from the victim's trade through controlled market manipulation.

The attack is predicated on the public mempool, where pending transactions are broadcast before inclusion in a block. Frontrunners use sophisticated node infrastructure to monitor this pool for profitable opportunities, such as large DEX swaps or NFT mints. By observing a victim's transaction intent (e.g., buying Token A), the attacker can submit their own transaction with a higher gas price to ensure it is mined first, then immediately sell into the victim's buy pressure for a risk-free profit. This creates a toxic environment for regular users.

Generalized frontrunning manifests in several predictable patterns:

• Sandwich Attacks: The most prevalent form. The attacker places one transaction before and one after a victim's large DEX trade, trapping it between their own buy and sell to capture the spread.
• Liquidity Sniping: On new token launches or pool creations, bots instantly buy at the initial price before liquidity is added, then sell at the inflated market price.
• Arbitrage Disruption: Bots frontrun legitimate arbitrage opportunities between exchanges, stealing the profit from the arbitrageur who discovered the price discrepancy.
• NFT Mint Frontrunning: Bots monitor for popular NFT mint transactions and submit their own mint transactions with higher gas to secure assets before the original minter.

Blockchain protocols and DApp designs are evolving to resist frontrunning:

• Flashbots & SUAVE: Systems like Flashbots Auction create a separate, private channel (searcher-builder separation) for submitting transactions, removing them from the public mempool. SUAVE aims to decentralize this process further.
• Commit-Reveal Schemes: Users submit a hashed commitment of their intent first. Only after a delay do they reveal the actual transaction, making real-time frontrunning impossible.
• Fair Sequencing Services: Proposed mechanisms where validators or sequencers order transactions based on receipt time rather than gas price, though this introduces centralization trade-offs.
• Private Transaction Pools: RPC services like Tenderly and BloxRoute offer private channels to submit transactions directly to block builders, bypassing public peer-to-peer gossip.

End-users and developers can adopt strategies to minimize exposure:

• Use DEX Aggregators: Aggregators like 1inch and CowSwap often use batch auctions or internal solvers that are less susceptible to sandwich attacks.
• Adjust Slippage Tolerance: Setting a very low, custom slippage (e.g., 0.1-0.5%) can prevent most sandwich attacks, though it may cause trades to fail in volatile markets.
• Limit Order Protocols: Using platforms with off-chain order matching (e.g., UniswapX) or limit orders removes transactions from the on-chain mempool until they are executable.
• Submarine Sends: A technique where a transaction is sent with a low gas price but a high max priority fee, making it unattractive to frontrun until a benevolent block builder includes it.

Frontrunning exists in a legal and economic gray area. In traditional finance, it is illegal for brokers to trade ahead of client orders. In decentralized finance, it's often viewed as a Maximal Extractable Value (MEV) inevitability, reframed as a network resource. The ecosystem has largely responded with technical, rather than legal, solutions. The economic impact is significant, with estimates of hundreds of millions extracted annually, leading to network congestion and increased costs for all users. This has spurred the growth of a professional searcher and block builder industry focused on capturing and, in some cases, redistributing this value.

A sandwich attack is the most common and recognizable form of generalized frontrunning on AMMs like Uniswap. The attacker executes two transactions that surround a victim's trade:

1. Frontrun: Buys the asset first, driving its price up.
2. Backrun: Sells the asset immediately after the victim's trade, profiting from the inflated price.

The victim receives a worse price due to slippage, and the attacker pockets the difference. This is a direct, automated exploitation of public transaction intent.

A commit-reveal scheme is a cryptographic technique used to hide transaction details during submission. It is a protocol-level defense against frontrunning.

• Commit Phase: A user submits a transaction with only a hash of their intent and a deposit.
• Reveal Phase: After a delay, the user submits a second transaction that reveals the actual details.

Since the searcher cannot decipher the intent from the initial commit transaction, they cannot frontrun it. This method adds latency and complexity but provides strong privacy guarantees.

Fair Sequencing Services are proposed decentralized protocols that define a canonical, fair order for transactions before they are executed. The goal is to prevent orderflow manipulation by searchers.

Projects like Astria and Espresso are building shared sequencers that use consensus mechanisms (e.g., Tendermint) to order transactions based on objective criteria like time of arrival, rather than the highest bid. This aims to return transaction ordering power from the block builder market to a decentralized set of rules.