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Aptos Launches Encrypted Mempool to Prevent Frontrunning and Sandwich Attacks

Cointelegraph · Felix Ng

Layer-1 blockchain Aptos has introduced an optional encrypted mempool feature designed to shield traders and institutions from maximal extractable value (MEV) attacks like frontrunning and sandwich attacks. Now live on Aptos’s mainnet, this feature encrypts pending transactions until their order within the block is finalized, enhancing transaction privacy and security for users such as institutional traders, AI agents, and builders.

Introduction of Aptos’s Encrypted Mempool

Aptos has rolled out an encrypted mempool feature available now on its mainnet, aimed at protecting users from MEV attacks such as frontrunning and sandwich attacks. Users can opt in to have their transactions encrypted locally before submission, keeping sensitive details like asset types and amounts hidden while still displaying the sender’s address and gas fee.

This is significant because in typical blockchain networks, mempools show all pending transaction data openly, allowing bots to observe upcoming transactions, front-run them, or mimic strategies, extracting value unfairly.

Mechanics of the Encrypted Mempool

When opted in, compatible wallets encrypt transaction data client-side before sending it to Aptos’s mempool. Validators then receive these encrypted transactions and agree on their ordering within a block without accessing their details.

Only after finalizing the order do validators collectively decrypt the transactions for execution and recording on-chain. Importantly, no single validator can decrypt a transaction alone, ensuring confidentiality and resistance to malicious internal actors.

This encrypted ordering prevents bots from preemptively reading and exploiting pending transactions, mitigating MEV risks like sandwich attacks.

Background on MEV and Sandwich Attacks

Maximal Extractable Value (MEV) attacks, including sandwich attacks, have caused considerable financial losses on various blockchains. Cointelegraph’s analysis shows Ethereum traders lost approximately $60 million to sandwich attacks over a year through October 2025, while Solana saw $29.7 million drained by similar attacks over 11 months.

Such attacks exploit transaction visibility by placing transactions just before and after a victim’s trade to profit from price movements, disadvantaging regular traders and undermining trust.

Technical Considerations and Performance

Using the encrypted mempool involves increased transaction costs—minimum gas prices are set at double the network’s base fee—and minor latency, adding roughly 20 milliseconds per block in tests conducted with 50 nodes.

Though these represent trade-offs, the enhanced protection from MEV manipulation presents a compelling value proposition for institutional users and others concerned about transaction front-running and privacy.

Comparison with Other MEV Mitigation Solutions

Aptos joins a growing list of blockchains addressing MEV. Gnosis Chain introduced a similar encrypted mempool in July 2024 based on Shutter technology, while Ethereum offers Flashbots Protect, a solution for privately routing transactions to block builders.

What sets Aptos apart is the approach of its existing validator set managing both transaction ordering and decryption together, eliminating reliance on separate decryption committees or trusted intermediaries. This design can simplify architecture and improve security assumptions.

Why it matters

Aptos’s encrypted mempool launch marks a critical advancement in mitigating maximal extractable value (MEV) attacks, which have led to tens of millions in losses for traders across major blockchains. By encrypting transaction details until their ordering is finalized, Aptos curtails the ability of bots and malicious actors to front-run or sandwich trades, safeguarding users’ assets and trading strategies. This development boosts confidence in the Aptos ecosystem and reflects a growing industry emphasis on transaction privacy and integrity. Although accompanied by modest cost and latency increases, such protection is especially vital for institutional-scale trades and sets a precedent for MEV defense mechanisms in blockchain ecosystems.

Prepared from the source material with AI-assisted editing and checked against the supplied facts.

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