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GMGN.Ai's 13-Hour MEV Showdown: 729 Users Compensated After Bots Evade Protections

GMGN.Ai's 13-Hour MEV Showdown: 729 Users Compensated After Bots Evade Protections

Bitget-RWA2025/10/28 06:28
By:Bitget-RWA

- GMGN.Ai reimbursed 729 users after a 13-hour MEV sandwich attack bypassed its BSC anti-sandwich nodes on October 25, 2024. - Funds were automatically credited by October 26 as co-founder Haze emphasized rapid loss calculations and transparent recovery protocols. - The incident highlights MEV's $1B+ 2024 impact on DeFi, with BSC's public mempools and high throughput making memecoins particularly vulnerable. - Experts urge continuous innovation in anti-MEV tools as attackers adapt tactics like delayed tran

GMGN.Ai, a

analytics and trading service, has completely compensated users who suffered losses from a recent maximal extractable value (MEV) sandwich attack that impacted 729 transactions on October 25, 2024. According to the platform’s co-founder, all affected users received automatic reimbursements within hours, with funds credited directly to their wallets by October 26, requiring no action on their part, as detailed in a . The exploit, which targeted GMGN’s anti-sandwich nodes on Binance Smart Chain (BSC), took place between 08:35 AM and 21:34 PM UTC on October 25, causing over 13 hours of disruption before node operations were restored, according to .

This event underscores the ongoing difficulties posed by MEV exploitation in decentralized finance (DeFi). MEV sandwich attacks typically involve bots identifying pending trades and executing transactions both before and after the victim’s order to manipulate prices, often resulting in unfavorable outcomes for users. In this instance, GMGN’s anti-sandwich nodes—intended to prevent such attacks—were circumvented, highlighting the increasing complexity of MEV tactics. Despite existing protections, the incident shows that even platforms with robust defenses can still be susceptible to sophisticated bot activity, as Coinotag noted.

GMGN.Ai's 13-Hour MEV Showdown: 729 Users Compensated After Bots Evade Protections image 0

GMGN’s handling of the situation has been commended for its rapid and open approach. Co-founder Haze stated that the team precisely calculated user losses and prioritized prompt compensation, automating the process for a smooth recovery. The platform also recommended that users activate anti-MEV features during trading, which group transactions together to make it harder for bots to detect pending orders. Analysts point out that while these tools are essential, MEV techniques are evolving quickly, necessitating ongoing improvements to defensive measures, as Coinotag reported.

The incident has sparked renewed debate about the wider effects of MEV on DeFi. Blockchain security data shows that MEV-related extractions surpassed $1 billion in 2024 alone, with BSC—favored by memecoin projects—being especially vulnerable due to its high transaction volume and open mempools. GMGN’s experience reflects a broader industry pattern, where even well-protected platforms face operational threats from MEV exploits. Experts such as DeFi analyst ZachXBT emphasize that anti-MEV solutions must keep pace with new attack methods, including delayed transaction bundling, which can leave systems exposed, as highlighted by Coinotag.

For GMGN, this episode highlights the need to balance technological advancement with robust security. While the platform’s quick compensation and openness have strengthened user confidence, the breach also raises concerns about the sufficiency of current anti-MEV strategies. The company has committed to thoroughly investigating the incident and upgrading its node infrastructure. Meanwhile, users are encouraged to adopt additional safeguards, such as tracking gas prices and using private relays, to reduce their vulnerability to MEV threats.

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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.

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