The Ethereum ecosystem protocols Liquid Collective and Obol Network have jointly released a risk report related to Ethereum staking, including concerns about client, operator, and cloud diversity, as well as potential risks associated with the 2025 Pectra upgrade.
According to Cointelegraph, Matt Leisinger, Chief Product Officer of Liquid Collective’s software development company Alluvial, stated in an interview that the report mentioned potential risks if issues arise with clients and operators, which could put Ethereum at risk. The report specifically mentioned the widely used client Geth, which holds an 84% market share, warning that significant errors in mainstream clients could lead to “severe penalties” and network instability.
Staking is an important component of Ethereum’s consensus mechanism, and the report highlighted the risks of staking assets facing downtime and penalties if staking is conducted through a single large node operator. The report emphasized the importance of operator diversity in maintaining network health and avoiding single points of failure.
Additionally, the report analyzed the geographic distribution of validators and cloud providers, cautioning against centralized distributions that could lead to related interruption risks. The report also suggested that Distributed Validator Technology (DVT) could effectively improve cloud and geographic diversity, serving as a fault-tolerant technology for multiple operator staking configurations and helping to prevent interruptions caused by natural disasters or wars.
On the other hand, the upcoming Ethereum “Pectra upgrade” scheduled for the end of the first quarter of 2025 will introduce EIP-7251 (maxeb, increasing the maximum effective balance of validators from 32 to 2,048 ETH, incentivizing staking operators to consolidate validators into a super validator). However, the report warned that increasing the maximum effective staking amount for individual validators to 2,048 ETH could reduce the total number of validators on Ethereum, potentially increasing the risk of single points of failure and affecting the network’s decentralization.
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