Cross-Chain Frontrunning

The core vulnerability enabling this attack is the time delay between transaction submission on a source chain and its verification/relay to a destination chain. This creates a race condition.

• Message Verification Time: Proof generation and relay for cross-chain messages (e.g., via LayerZero, Wormhole, Axelar) can take seconds to minutes.
• Finality Differences: Chains with probabilistic finality (e.g., Ethereum) have a confirmation delay, while some destination chains (e.g., Solana, Avalanche) have sub-second finality, giving attackers a window.

Price feed oracles like Chainlink are critical targets. A large pending trade on one chain will affect the asset's price, which oracles periodically update and broadcast to other chains. Attackers front-run the oracle update.

• Mechanism: The attacker anticipates the new price that will be reported after the victim's trade settles. They execute trades on other chains that use the same oracle before the new price is posted, exploiting the stale price.

The liquidity pools on the destination chain's decentralized exchanges (DEXs) are the direct financial target. The attacker's profit is extracted from these pools.

• Target Characteristics: Pools with lower liquidity are more susceptible to significant slippage from the victim's trade, creating larger arbitrage opportunities.
• Multi-Pool Attacks: Sophisticated bots may route through multiple pools or use flash loans on the destination chain to maximize the extracted value before the price corrects.

Protocols that use cross-chain collateral or price feeds for loan health calculations are vulnerable. An attacker can manipulate the perceived collateral value or asset price across chains to create unsafe loans or trigger unnecessary liquidations.

• Example: Frontrunning a large deposit that increases the collateral value of an asset on Chain A could allow an attacker to borrow more against it on Chain B before the price update, or trigger a liquidation on a competing position.

Several mechanisms aim to reduce the viability of cross-chain frontrunning:

• Threshold Encryption: Using services like Shutter Network to encrypt transaction content until it is included in a block, hiding intent from the mempool.
• Fair Sequencing Services (FSS): Dedicated sequencers that order transactions randomly or via first-come-first-serve, preventing Gas Auction races.
• Commit-Reveal Schemes: Users submit a commitment hash first, then reveal the transaction details later, obscuring the profitable opportunity until it's too late to frontrun.

Cross-chain transactions are not atomic; they involve sequential steps across separate state machines. This creates a temporal vulnerability window where a transaction is visible on the source chain but not yet finalized on the destination chain. Attackers can monitor mempools on both sides, analyze the intent (e.g., a large swap), and insert their own transaction with higher fees to execute first, stealing the expected profit.

Many cross-chain bridges rely on off-chain relayers or oracles to attest to events. These become prime targets.

• Data Withholding: A malicious relayer can see a user's transaction, withhold the attestation, and frontrun it on the destination chain.
• Signature Spoofing: If a relayer's signing key is compromised, an attacker can forge fraudulent state attestations to enable frontrunning attacks they control.
• Sequencer Centralization: In optimistic or zk-rollup bridges, a centralized sequencer has full view of pending cross-chain messages, creating a single point of failure for frontrunning.

Bridges with on-chain liquidity pools (e.g., AMM-based bridges) are vulnerable to MEV extraction similar to decentralized exchanges. An attacker observing a large deposit or withdrawal intent can:

• Frontrun the deposit to buy the asset before the user's transaction inflates its price.
• Backrun the withdrawal to sell into the expected price impact. This is exacerbated by bridge-specific liquidity pools that may be thinner and easier to manipulate than mainnet DEXs.

Frontrunning evolves into sophisticated cross-chain MEV supply chains. Searchers use specialized infrastructure:

• Cross-Chain Mempool Monitoring: Bots track pending transactions across multiple chain mempools simultaneously.
• Gas Auction Spillover: A high-gas auction on Chain A signals a profitable opportunity on Chain B, triggering a bidding war on the destination chain.
• Bundle Propagation: Attackers build transaction bundles that execute atomically across chains via bridges, locking in arbitrage profits and making them unstoppable once initiated.

Inherent design choices in cross-chain protocols create frontrunning risks:

• Time-Locked Vaults: Bridges that use time delays for withdrawals (e.g., 24-hour challenge periods) publicly reveal withdrawal intentions, giving attackers a long window to plan frontrunning.
• Price Oracle Latency: Bridges that use price oracles with slow update cycles (e.g., every 30 minutes) can be frontrun when a new price is about to be posted.
• Non-Atomic Swaps: If a cross-chain swap is split into separate deposit and claim transactions, the claim transaction is highly vulnerable to being frontrun.

Protocols employ several methods to reduce cross-chain frontrunning risk:

• Threshold Encryption: Encrypting transaction details (e.g., with Ferveo) until execution, hiding intent from public mempools.
• Commit-Reveal Schemes: Users submit a commitment hash first, then reveal transaction details later, making frontrunning impossible until the reveal.
• Fair Ordering Protocols: Using consensus mechanisms like Tempo or Aequitas to order transactions fairly, rather than by gas price.
• Private Mempools / Submarines: Routing transactions through private channels (e.g., Flashbots SUAVE, Taichi Network) to avoid public exposure.

This cryptographic technique hides transaction details during the submission phase to prevent bots from reading and copying them. Users submit an encrypted or hashed version of their transaction (the commit). After a predefined delay, they reveal the plaintext details. This creates a fair ordering window where transactions are batched and settled simultaneously, neutralizing the advantage of faster network speeds. It's a core mechanism in protocols like SUAVE.

A dedicated, trusted sequencer node receives transactions and orders them before they are finalized on-chain. By controlling the transaction queue, it can enforce rules like First-Come, First-Served (FCFS) based on the time it receives the transaction, not its network propagation time. This prevents gas auction wars and time-bandit attacks. Solutions range from centralized sequencers (fast, trusted) to decentralized validator sets using MEV-boost-like architectures for censorship resistance.

User-side parameters that limit the impact of frontrunning. Slippage tolerance sets the maximum acceptable price movement for a swap. Transaction deadlines specify a time window after which the transaction fails if not executed. On cross-chain routes, these must be carefully calibrated for the source chain block time, bridge latency, and destination chain congestion. Aggregators like LI.FI and Socket use these to revert transactions that would be victim to harmful frontrunning.

Securing the message-passing layer between chains is fundamental. This involves:

• Validator/Relayer Security: Using economically secured validator sets (e.g., EigenLayer, Babylon) or optimistic/zk-proof systems to attest to transaction batches.
• Incentive Alignment: Designing relayer rewards that penalize malicious ordering (e.g., slashing) and reward honest, timely delivery.
• Redundancy: Employing multiple, independent relayers to avoid single points of failure and manipulation. Protocols like Axelar and Wormhole implement these principles.

Instead of allowing extractable value (MEV) to be captured stealthily by bots, this strategy transparently auctions off the right to order a block or batch of cross-chain transactions. The revenue from this auction can then be redistributed back to the users whose transactions were included or to the protocol's treasury. This turns a negative externality (value extraction) into a potential protocol revenue stream or user rebate, formalizing the economics of cross-chain sequencing.

Protections implemented on the receiving chain to neutralize malicious transactions that slipped through. Key techniques include:

• Pre-execution Checks: Simulating the transaction outcome upon receipt and reverting if it violates user parameters.
• Private RPCs & Direct Bundling: Using private transaction relays or submitting transactions directly to block builders via Flashbots Protect-like services on the destination chain to avoid public mempool exposure.
• Dynamic Fee Estimation: Advanced fee prediction that accounts for potential frontrunner activity, ensuring the transaction is sufficiently incentivized to be included fairly.

The foundational concept behind frontrunning. MEV refers to the total value that can be extracted from block production beyond standard block rewards and gas fees, primarily by reordering, inserting, or censoring transactions within a block. Cross-chain MEV extends this concept to exploit opportunities that span multiple blockchains.

• Forms: Arbitrage, liquidations, sandwich attacks.
• Key Actors: Searchers, validators, and builders who compete to capture this value.

The primary infrastructure enabling cross-chain transactions and, consequently, cross-chain frontrunning. Bridges are protocols that facilitate the transfer of assets and data between different blockchain networks.

• Vulnerability: The multi-step, time-delayed nature of most bridge designs (e.g., lock-mint, burn-mint) creates a window of opportunity for attackers to observe pending transactions on one chain and act on another.
• Examples: Token bridges, message-passing protocols like IBC, and liquidity networks.

A specific class of MEV attack that exploits the probabilistic finality of some blockchains. In a time-bandit attack, a validator or miner attempts to reorganize the blockchain's recent history (a reorg) to capture MEV that would have been lost.

• Cross-Chain Impact: If a transaction on Chain A is finalized slowly, an attacker might try to reorg Chain A to invalidate a cross-chain message already acted upon on Chain B, leading to double-spends or stolen funds.
• Defense: Protocols rely on strong finality guarantees or increased confirmation wait times.

A cryptographic primitive used to secure many cross-chain bridges and mitigate frontrunning. In a TSS, a group of validators collaboratively generates a single signature to authorize a cross-chain transaction, without any single party holding the complete private key.

• Security Model: Reduces trust by distributing key control among a decentralized set of signers.
• Limitation: While it prevents single-point key theft, it does not inherently prevent frontrunning if the signing process is observable or predictable before the transaction is finalized on-chain.

A proposed solution to MEV and frontrunning that aims to enforce a fair, canonical ordering of transactions before they are executed. FSS uses a decentralized network to order transactions, preventing manipulative reordering.

• Cross-Chain Application: An FSS could be applied to the message queue of a cross-chain bridge, ensuring that the first valid message observed is the one processed, eliminating the race condition that frontrunners exploit.
• Challenge: Requires robust consensus on transaction ordering across potentially untrusted nodes.

A related attack vector that often intersects with cross-chain operations. Oracles provide external data (like price feeds) to smart contracts. Manipulating this data can create profitable, illegitimate cross-chain opportunities.

• Frontrunning Link: An attacker might frontrun a large oracle update on one chain to exploit a dependent protocol (e.g., a lending market) on another chain that uses the same price feed.
• Example: Causing a false liquidation on Chain B by manipulating the price feed sourced from Chain A.