What happens when a crypto liquidity provider meets MEV

Part of the ‘Liquidity issues in crypto’  series, this article breaks down MEV, the JIT liquidity attack on LP fees, and what Aqua changes for crypto liquidity providers.

Maximal Extractable Value (MEV) is an umbrella term  for strategies that extract value by exploiting how transactions are ordered and included in blocks.

Sandwich attacks are the best-known example because they directly exploit a trader’s price impact and slippage. For crypto liquidity providers, though, that’s often not the most pressing issue.

A more relevant concern in certain market conditions can be a just-in-time liquidity attack (JIT), sometimes described as an LP sandwich. Instead of targeting a trader’s slippage, it targets the LP’s fee share. Liquidity is added right before a large swap and removed right after, so the swap’s fees get captured by the fastest liquidity rather than by liquidity that stayed in the pool over time. 

For a crypto liquidity provider, this is a structural issue in crypto liquidity pools, not a one-off mistake.

When large swaps are easy to observe, fee capture can favor the fastest liquidity. As a result, long-term LPs sometimes see lower fee income, depending on the pool, market conditions and competitive dynamics.

MEV and the LP problem

In a typical swap, traders pay fees to a liquidity pool. LPs earn those fees for supplying inventory and taking price risk.

MEV enters when a swap can be anticipated and someone can successfully position around it by getting their action included in time, often by taking advantage of transaction visibility and ordering.

Sometimes that positioning uses trades, the classic sandwich. Sometimes it uses liquidity, just in time liquidity, or JIT. That second case is the more direct LP problem. 

Recent academic research has started to quantify this dynamic. A September 2025 paper builds a transaction-level model of JIT in concentrated liquidity AMMs and shows that, under strategic deployment in the model, passive LP profits can be eroded by up to 44% per trade. 

Crypto liquidity pools and MEV: same umbrella, different victim

Classic sandwich, trader is the victim: a bot trades before and after a user swap to push the price against the user and capture the price move. This extracts value from execution quality.

JIT liquidity, LP is the victim: no trade is needed. Liquidity is added only for the moment fees are about to be paid and removed immediately after. This extracts value from fee distribution.

What JIT looks like

In public mempools, a large swap is visible before it executes.

1. Liquidity is added right before the swap, often concentrated in the exact range the price will cross.
2. The swap executes and pays fees.
3. Because fees are distributed in proportion to in-range liquidity at execution, this temporarily dilutes passive LPs’ fee share.
4. Liquidity is removed immediately after.

Result: the JIT actor captures a large share of the fees from that swap, while passive LPs earn less than expected despite providing liquidity over time.

What reverse JIT can look like

Reverse JIT patterns can occur when an attacker anticipates a pending liquidity addition, such as a large deposit into a pool. In these situations, the attacker may frontrun with a swap that temporarily moves the price, causing the LP to deposit at a less favorable rate, and then backrun afterward. This can lead to immediate impermanent loss for the LP.

AMMs allow liquidity to be added or removed at any time, which is a core feature of DeFi. That same flexibility can make it possible for opportunistic liquidity to appear primarily when fees are about to be collected. In some pool designs, JIT liquidity may reduce slippage for traders, but this effect is situational rather than universal. The main cost is typically borne by passive LPs, because fee capture can favor speed and timing rather than sustained liquidity provision.

Crypto market liquidity: factors associated with JIT

 JIT tends to thrive when a pool has:

• visible orderflow, meaning swaps can be observed before execution
• large swaps relative to active liquidity in the relevant price range
• predictable execution patterns that make timing and range placement easier
• incentives that concentrate volume into specific windows or events

In some situations, JIT may also appear when market liquidity is thin and large trades generate disproportionately high fees. However, stressed conditions can also increase volatility and execution risk, so outcomes can vary widely.  

What changes with Aqua

JIT attacks exist because fee distribution in AMMs is moment based. Fees are paid to whoever provides liquidity at the exact moment a swap crosses their range. That makes it profitable for attackers to appear only for the fee moment and leave immediately after.

Aqua changes this at the structural level.

In Aqua, liquidity is not pooled in a shared AMM where fees are split across everyone who happens to be active. Each strategy belongs to a specific LP, and all fees generated by that strategy go only to that LP. No external liquidity can be added to that strategy. That means MEV bots cannot jump in before a trade to capture fees and cannot dilute or front run the LP’s position.

Aqua also removes the mechanics that make reverse JIT possible. LPs do not deposit tokens into a pool. Instead, they grant a strategy permission to use their tokens when needed. Because there is no on-chain deposit and withdrawal of liquidity into shared pools, attackers cannot exploit timing around liquidity entry and exit to extract fees.

Together, this eliminates the core JIT incentive. There is no shared fee moment to target, and no way for outsiders to insert liquidity into an LP’s strategy at the last second. Fees are always attributed to the LP who owns the strategy, regardless of when swaps happen.

Aqua is already live for developers. Builders can use the 1inch Aqua Protocol today, while the shared liquidity layer and strategy tooling continue to evolve.

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