MEV (Maximal Extractable Value) in Bridges

The primary MEV opportunity in bridges. It exploits price discrepancies for the same asset (e.g., ETH) on different chains. A searcher can:

• Borrow asset A on Chain 1.
• Bridge it to Chain 2 where its price is higher.
• Sell it on Chain 2.
• Bridge the proceeds back to Chain 1.
• Repay the loan and keep the profit, assuming the arbitrage spread exceeds bridge fees and gas costs.

MEV bots monitor for undercollateralized positions on lending protocols on one chain. When a position becomes eligible for liquidation, a searcher can:

• Front-run the public liquidation transaction.
• Source the required collateral asset via a fast bridge from another chain.
• Execute the liquidation on the target chain to claim the liquidation bonus.
• This creates a race condition dependent on cross-chain message latency.

Many bridges rely on oracles or off-chain relayers to attest to an event's occurrence on the source chain. Adversaries can exploit this by:

• Creating a profitable derivative position (e.g., a futures contract) whose payout depends on a bridged price feed.
• Manipulating the oracle's data submission or delaying/accelerating the inclusion of a specific block containing the price data on the destination chain to benefit their position.
• This attacks the trust assumptions and liveness of the bridge's validation mechanism.

Occurs in bridges with optimistic or fraud-proof designs that have a challenge period. A malicious actor could:

• Propose an invalid state root or transaction batch.
• Simultaneously execute trades based on the anticipated (but incorrect) new state on the destination chain.
• Withdraw profits via another bridge before the fraud proof is submitted and the invalid state is reverted.
• This exploits the temporal window between state proposal and finality.

In bridges with centralized sequencing or relaying (e.g., many Layer 2 bridges), the privileged operator controls transaction ordering for cross-chain messages. This allows them to:

• Extract traditional MEV (e.g., DEX arbitrage, sandwich attacks) by reordering the transactions they relay.
• Censor specific cross-chain messages.
• This represents a centralization risk and turns the bridge operator into a potential monopolistic MEV extractor.

Bridge MEV directly impacts the system's security model:

• Extracted Value as a Security Cost: MEV profit is value drained from bridge users, acting as a latent tax.
• Incentive Misalignment: Validators/Relayers may be incentivized to cause delays or reorgs to create MEV opportunities, harming reliability.
• Attack Financing: Profits from bridge MEV can be used to fund correlated attacks on the underlying consensus or bridge itself, especially if the extracted value exceeds the staking/slashing penalties.

Relayers are network participants responsible for submitting transaction data (e.g., Merkle proofs) from a source chain to a destination chain. In many bridge designs, they compete to be the first to submit valid proofs, creating a race where latency and gas bidding determine the winner. This competition is a primary source of latency arbitrage MEV, as the first successful relayer claims the protocol fee or reward.

• Role: Data transmission and proof submission.
• Incentive: Protocol fees, transaction tips, or native token rewards.
• Example: In an optimistic rollup bridge, the first relayer to submit a state root after the challenge period earns the relayer reward.

In bridges connected to rollups or other settlement layers, Sequencers (or Proposers) order transactions on the source layer before creating a batch for bridging. They have privileged control over transaction ordering, enabling them to extract ordering-based MEV (like frontrunning or sandwich attacks) within the batch that will be bridged. Their role makes them pivotal actors in cross-chain MEV supply chains.

• Role: Order and batch transactions for bridging.
• MEV Source: In-batch arbitrage, frontrunning user bridge transactions.
• Example: A rollup sequencer can frontrun a large cross-chain swap intent before including it in a batch bound for a decentralized exchange on the destination chain.

For bridges using consensus-based security models (e.g., multi-signature schemes, validator committees), Validators or Guardians are the entities that sign off on the validity of cross-chain messages. While their primary role is security, their ability to censor or order message approval can be a vector for MEV. In some models, they may also act as the final relayer.

• Role: Provide attestations or signatures for bridge operations.
• MEV Vector: Transaction censorship, approval latency manipulation.
• Example: In a threshold signature bridge, validators could delay signing a lucrative arbitrage opportunity to capture it themselves.

Searchers are independent, automated agents that scan multiple chains for profitable MEV opportunities arising from bridge latency or price discrepancies. They execute complex strategies, often outbidding standard relayers for priority. They are the pure extractors of Bridge MEV, engaging in cross-chain arbitrage and liquidations.

• Role: Identify and exploit profitable cross-chain discrepancies.
• Strategies: Arbitrage between DEXes on different chains, liquidating undercollateralized positions across chains.
• Tooling: Use sophisticated MEV bots, private RPCs, and flash loans.

Users initiating cross-chain transactions are often the source of MEV opportunities and can be negatively impacted by it. A user's transaction revealing intent (e.g., a large swap) can be frontrun. However, users can also benefit by participating in MEV redistribution mechanisms or using privacy-preserving bridge protocols.

• Role: Initiate transactions that create MEV opportunities.
• Impact: Often pay implicit costs via worse execution (slippage, frontrunning).
• Counter-strategy: Use encrypted mempools, commit-reveal schemes, or direct integrations with searcher networks for better execution.

Bridge Aggregators (like Li.Fi, Socket) and Cross-chain Builders are advanced actors that bundle multiple user intents and route them through optimal paths. They can internalize Bridge MEV by finding the best execution across chains and capturing the difference between quoted and actual execution prices. They act as sophisticated intermediaries between users and the bridge infrastructure.

• Role: Optimize and execute complex cross-chain routes.
• MEV Relation: Capture route arbitrage and improve user execution.
• Example: An aggregator splits a large swap across three bridges and multiple DEXes, capturing the spread while guaranteeing the user a net-better rate.

Attackers monitor the mempool of the source chain for pending bridge transactions. By paying higher gas fees, they can frontrun a user's deposit transaction to manipulate the exchange rate or asset price before the user's transaction settles. On the destination chain, they can sandwich the minting transaction to extract value from slippage.

• Example: An attacker sees a large swap-and-bridge transaction for ETH→USDC. They frontrun it to buy USDC cheaply, then sell it back after the user's large trade moves the price.

Validators or sequencers controlling the bridge's relayer network or destination chain block production can censor transactions to create MEV opportunities. They may delay the submission of proof for a profitable user transaction until market conditions are favorable for their own arbitrage.

• Risk: This undermines the liveness guarantee of a bridge, as transactions are not processed in a timely or fair manner. It can be used to trap funds during volatile markets.

Many bridges rely on oracles or validators to attest to asset prices or events on the source chain. An attacker with control over this data feed (e.g., via a 51% attack or compromised validator) can report false data.

• Attack Vector: Reporting an incorrect exchange rate to mint far more assets on the destination chain than deposited, or to steal funds from liquidity pools. This is a direct theft vector beyond mere value extraction.

In bridges using optimistic rollups or ZK-rollups, the sequencer or block proposer has significant control over transaction ordering within the rollup. They can extract MEV from cross-chain messages (e.g., deposits/withdrawals) before they are finalized on Layer 1.

• Mechanism: The sequencer can reorder the execution of a bridge withdrawal to frontrun a user's subsequent trade within the rollup's decentralized exchange, capturing arbitrage profits.

Bridges that use Automated Market Maker (AMM) pools on the destination chain are vulnerable to classic DEX arbitrage MEV. When a bridge mints assets, it often deposits them into a liquidity pool, creating a temporary price imbalance.

• Extraction: MEV bots immediately execute arbitrage trades against this pool to profit from the price discrepancy, costing the bridge's liquidity providers or users through increased slippage and worse effective exchange rates.

Bridge designers employ several techniques to mitigate MEV risks:

• Threshold Encryption: Encrypting transaction details (like amounts) until they are included in a block.
• Fair Ordering Protocols: Using consensus mechanisms like FCFS (First-Come-First-Served) or Tee (Trusted Execution Environments) to prevent reordering.
• Decentralized Relayer Networks: Reducing reliance on a single, censorable sequencer.
• Succinct Proofs: Using ZK-proofs to validate state without revealing exploitable transaction data in the mempool.

A cryptographic technique where user transactions are encrypted until a commit-reveal scheme is completed. This prevents MEV searchers from observing pending transactions on the destination chain, blocking front-running and sandwich attacks. The bridge's relayers or a decentralized network of oracles hold decryption keys and only reveal the transaction after it has been finalized for inclusion in a block.

A security model, inspired by optimistic rollups, that introduces a challenge period after a bridge transaction is proposed. During this window, any watcher can submit a fraud proof to dispute an invalid state transition. This delays finality but creates a strong economic disincentive for validators to extract MEV through malicious actions, as their bonded stake can be slashed.

Protocols that enforce a canonical order of transactions within a block or a batch, removing the power of block producers to reorder transactions for MEV. Bridges can integrate with fair sequencing services (FSS) or use consensus mechanisms like Tendermint that provide deterministic finality, ensuring users are protected from time-bandit attacks and transaction reordering.

A mechanism where multiple user swap requests are aggregated into a single batch and executed at a single, uniform clearing price. This eliminates the price impact and slippage differences between transactions in the same batch that MEV bots typically exploit. Bridges using automated market makers (AMMs) or request-for-quote (RFQ) systems can implement this to ensure all users in a batch receive the same rate.

Using a decentralized network of permissioned or incentivized relayers to propose blocks or order transactions. A prominent concept is SUAVE (Single Unified Auction for Value Expression), a dedicated blockchain that aims to decentralize the block building process. Bridges can use such networks to create a competitive, MEV-aware marketplace for transaction inclusion that benefits users.

A strategic design choice where a bridge routes transactions to chains with inherent MEV resistance. This includes selecting destination chains with:

• Fast finality (e.g., BFT-based chains) to reduce reorg risk.
• High decentralization of validators to reduce collusion risk.
• Native encrypted mempools or proposer-builder separation (PBS). This shifts the mitigation burden to the underlying chain's consensus layer.

Specialized bots monitor price discrepancies for the same asset (e.g., ETH, USDC) across different chains (Ethereum, Arbitrum, Polygon). They execute a classic arbitrage strategy:

• Identify: Spot a higher price for USDC on Chain A vs. Chain B.
• Bridge & Swap: Bridge USDC from Chain B to Chain A, then sell at the higher price.
• Profit: Capture the price delta minus bridge fees and gas costs. This activity provides liquidity rebalancing but can also lead to latency races and increased gas fees on the destination chain during execution.

Protocols like MakerDAO use cross-chain oracles for collateral prices. A sharp price drop on one chain can trigger liquidations. MEV searchers monitor for these events and engage in cross-chain MEV by:

• Frontrunning the liquidation call on the target chain.
• Bridging assets to cover the debt at high speed.
• Profiting from the liquidation penalty. This creates a complex race where success depends on bridge finality time and oracle update frequency, potentially exacerbating market volatility.

The Wormhole bridge uses a set of Guardian nodes to attest to cross-chain messages. While the protocol itself is designed to be trust-minimized, the attestation process creates a potential MEV vector. Observers of the Guardian network could gain a few seconds of advance knowledge on large, pending cross-chain swaps or governance actions. This information asymmetry could theoretically be used for frontrunning on the destination chain before the transaction is finalized, highlighting the MEV risks inherent in any bridging mechanism with observable message queues.

With regulators targeting privacy tools and specific addresses, MEV can emerge around compliance. If a bridge relayer is forced to censor transactions from sanctioned addresses, it creates a new extractable value opportunity:

• Searchers could monitor the public mempool for censored transactions.
• They could then offer a side-channel or alternative, non-compliant bridge service for a premium fee.
• This transforms regulatory censorship into a profitable arbitrage on access to cross-chain liquidity, a form of policy-based MEV.