MEV Watch 2026: Tracking AI Front-Running on Ethereum L2s

What MEV Watch tracks today

MEV Watch operates as a transparency layer for the Ethereum MEV-Boost network. It monitors relay behavior to quantify censorship and measure how much value is extracted from block production. By tracking the flow of transactions through relays, the tool reveals which entities are filtering out profitable trades and how that impacts network fairness.

The platform focuses on the mechanics of MEV extraction, specifically the role of relays in the MEV-Boost ecosystem. Relays sit between block builders and validators, aggregating blocks to maximize validator rewards. MEV Watch logs daily metrics on which relays censor transactions, providing a public ledger of this activity. This data helps validators and users assess the health of the MEV-Boost network.

Understanding these flows is critical because MEV threatens the decentralization of blockchain systems. When specific relays consistently censor transactions, they gain disproportionate influence over transaction ordering. MEV Watch exposes these patterns, allowing the community to identify potential bottlenecks or centralized points of failure in the block production pipeline. For more context on the broader definition of MEV, see the ethereum.org MEV documentation.

AI front-running changes the attack surface

The landscape of Maximal Extractable Value (MEV) on Ethereum Layer 2s is shifting from simple arbitrage to complex, adaptive front-running strategies. Early MEV bots relied on predictable patterns, such as sandwiching trades around large limit orders. Today, AI-driven searchers use machine learning models to predict user intent and network congestion in real-time, creating a more sophisticated threat vector that challenges L2 security assumptions.

These AI agents operate like high-frequency predators, scanning the mempool for inefficiencies that human operators or rule-based scripts would miss. They analyze transaction signatures, gas price fluctuations, and historical block data to execute front-running attacks with millisecond precision. This evolution means that L2s, which often prioritize speed and low cost over rigorous transaction ordering, are increasingly vulnerable to exploitation.

The impact extends beyond financial loss for individual traders. As AI searchers become more efficient, they can extract value from even minor price discrepancies, effectively taxing every transaction on the network. This creates a hostile environment where legitimate users face higher slippage and delayed execution, while the network’s decentralization is threatened by the concentration of power among a few sophisticated AI operators.

Comparing relay censorship rates

The landscape of MEV-Boost relays is defined by a stark trade-off between transaction inclusion speed and censorship resistance. Not all relays are created equal; some act as open gateways for any valid transaction, while others enforce strict compliance filters, most notably those aligned with OFAC sanctions. For validators and users, understanding these differences is essential to determining whether the network remains permissionless or becomes subject to centralized control.

MEV Watch provides a transparent view into this dynamic by tracking the proportion of blocks each relay proposes. The data reveals a fragmented market where censorship is not binary but varies by relay policy. Some relays maintain a zero-censorship stance, prioritizing maximal transaction inclusion, while others explicitly block addresses associated with sanctioned entities. This divergence creates distinct user experiences depending on which relay a validator selects.

The table below compares major relays based on their reported censorship rates and block share. These metrics highlight the ongoing tension in the ecosystem: while open relays dominate block production volume, compliant relays continue to capture significant share, reflecting the pressure on validators to navigate regulatory risks.

Source: MEV Watch tracks daily metrics and relay leaderboards to provide this transparency. The data underscores that while censorship-resistant relays often hold the majority of block share, the presence of compliant alternatives remains a critical factor in the decentralization debate.

L2 Security Implications for Users

As Ethereum L2s like Arbitrum and Optimism handle the bulk of retail transaction volume, they have become the primary hunting grounds for AI-driven front-running. Unlike the base layer, where high gas costs often act as a natural barrier to entry, L2s offer cheap, fast execution that attracts high-frequency bots. These AI agents do not just wait for transactions to land; they actively scan the mempool to predict user intent, inserting their own transactions to profit from the resulting price movements.

For the average user, this manifests as degraded execution quality. When an AI bot detects a large buy order on a decentralized exchange (DEX), it can front-run the trade by purchasing the asset seconds earlier. This artificial demand drives up the price, forcing your original transaction to execute at a significantly worse rate. The result is unexpected slippage that erodes your capital without any visible warning in the interface.

Beyond slippage, AI front-running increases the likelihood of transaction failures. Sophisticated bots may engage in sandwich attacks, where they buy before your transaction and sell immediately after, manipulating the pool’s liquidity to ensure your trade fails or results in a loss. This is not merely a nuisance; it is a structural threat to the fairness of L2 networks. As MEV strategies become more automated, the cost of participating in these ecosystems rises, effectively taxing every user who interacts with the chain.

The security model of L2s is still evolving to counter these threats. While solutions like private transaction pools and commit-reveal schemes exist, they are not yet universally adopted. Until AI front-running is mitigated, users must assume that their trades are visible and vulnerable to exploitation by high-speed algorithms. This reality underscores the need for improved privacy layers and stricter regulatory oversight of MEV extraction practices.

How to protect transactions in 2026

As AI front-running grows more sophisticated, standard public mempool submission is no longer safe for sensitive trades. Users and developers must shift from passive participation to active transaction shielding. The goal is to remove the reordering advantage from searchers before the block is even built.

Use private transaction pools

Sending transactions through private mempools like Flashbots Protect or SUAVE keeps your order details invisible to public searchers. Instead of broadcasting to everyone, you route directly to validators or builders who agree not to inspect your payload until it is sealed. This effectively blinds the AI models that rely on public order flow to identify profitable front-running opportunities.

Switch to MEV-resistant DEXs

Not all decentralized exchanges are created equal when it comes to MEV. Standard AMMs like Uniswap V2 are highly vulnerable to sandwich attacks. Opt for protocols using Commitment Auctions, such as CowSwap, or order-flow auctions that bundle trades internally. These mechanisms match orders against each other rather than the public liquidity pool, removing the window for external bots to exploit price slippage.

Adopt MEV-resistant design patterns

For developers, architecture matters. Implementing slippage protections and using commit-reveal schemes for complex interactions can significantly raise the cost of attack. Additionally, integrating MEV-tolerant routing layers ensures that even if a transaction is exposed, the potential profit for an attacker is minimized below the cost of computation.

Common questions about MEV Watch

For a deeper technical breakdown, see the official MEV documentation on ethereum.org.