Aave hit as Mar. 13 USDT swap loss tied to MEV

What to Know:

• Extreme slippage allowed a MEV sandwich attack to drain $50.4M.
• Misconfigured slippage led to catastrophic loss during a $50.4M swap.

On March 13, 2026, a user attempted roughly $50.4 million to swap USDT for AAVE and ended up receiving about $36,000, as reported by Reddit. The transaction’s slippage tolerance was set in the 99–99.9% range, enabling execution at almost any price. In a public mempool, that combination exposed the order to a classic MEV sandwich attack.

Bots observed the pending swap, bought ahead to push AAVE’s price up, and then sold after the victim trade cleared. With extreme slippage tolerance, the victim route accepted severe price impact across thin liquidity. The back-run leg then captured the spread, leaving the user with only a fraction of the intended output.

Immediate impact: bots captured value; user received about $36,000

The immediate outcome was that arbitrage and MEV bots captured most of the trade’s value while the user’s wallet realized about $36,000 in AAVE. The event underscores that slippage tolerance is not a trivial setting; it governs worst-case execution.

In response discussions, the protocol’s maintainers emphasized interface safeguards and fee remediation to address user harm and prevent recurrence. “We will refund approximately $600,000 in protocol fees as a gesture of goodwill and hard block extreme slippage transactions,” said Aave Labs.

How MEV sandwich attacks exploit high slippage tolerance in public mempools

According to Flashbots, MEV sandwich attacks exploit visible pending transactions in public mempools by inserting a buy before and a sell after the victim’s trade. High slippage tolerance widens the allowable execution band, turning price impact into a profit window for the attacker. Private RPC or protected order flow can reduce exposure by hiding intent until inclusion.

According to CoW Swap, solver-based batch auctions route orders without broadcasting them to the public mempool and add price protection through competitive solvers. These techniques reduce, but do not eliminate, MEV risk, particularly when liquidity is shallow or volatility is high.