From the Clearing Storm to Cloud Downtime: The Crisis Moment of Crypto Infrastructure

Original Title: Crypto Infrastructure is Far From Perfect
Original Author: YQ, Crypto KOL
Original Translator: AididiaoJP, Foresight News

Amazon Web Services (AWS) has once again experienced a significant outage, severely impacting crypto infrastructure. Issues with AWS in the US East (Northern Virginia data center) led to the paralysis of Coinbase and dozens of other major crypto platforms, including Robinhood, Infura, Base, and Solana.

AWS has acknowledged an "increased error rate" affecting Amazon DynamoDB and EC2, which are core database and computing services relied upon by thousands of companies. This outage provides immediate and stark validation of the central argument of this article: the reliance of crypto infrastructure on centralized cloud service providers creates systemic vulnerabilities that repeatedly manifest under stress.

The timing is starkly instructive. Just ten days after a $19.3 billion liquidation cascade exposed infrastructure failures at the trading platform level, today’s AWS outage indicates that the problem extends beyond a single platform, reaching into the foundational layer of cloud infrastructure. When AWS fails, the cascading effects simultaneously impact centralized trading platforms, "decentralized" platforms with centralized dependencies, and countless other services.

This is not an isolated incident but a pattern. The following analysis documents similar AWS outages that occurred in April 2025, December 2021, and March 2017, each resulting in the paralysis of major crypto services. The question is not whether the next infrastructure failure will occur, but when it will happen and what the triggering factors will be.

April 10-11, 2025 Liquidation Cascade: Case Study

The April 10-11, 2025 liquidation cascade provides an instructive case study of the infrastructure failure pattern. At 20:00 UTC, a significant geopolitical announcement triggered a market-wide sell-off. Within an hour, $6 billion was liquidated. By the time the Asian markets opened, $19.3 billion in leveraged positions had evaporated across 1.6 million trader accounts.

Figure 1: Timeline of the October 2025 Liquidation Cascade

This interactive timeline graph shows the dramatic progression of liquidation amounts by the hour. In the first hour alone, $6 billion evaporated, followed by an even more severe acceleration in the second hour of the cascade. The visualization shows:

· 20:00-21:00: Initial Shock - $6 billion liquidated (red area)

· 21:00-22:00: Peak Cascade - $4.2 billion, at which point API rate limiting began

· 22:00-04:00: Continued Deterioration - $9.1 billion liquidated in a thin liquidity market

· Key Turning Point: API rate limits, market maker withdrawals, thinning order books

Its scale is at least an order of magnitude larger than any previous crypto market event, and historical comparisons illustrate the step-function nature of this event:

Figure 2: Historical Liquidation Event Comparison

The bar chart dramatically illustrates the prominence of the October 2025 event:

· March 2020 (COVID): $1.2 billion

· May 2021 (Crash): $1.6 billion

· November 2022 (FTX): $1.6 billion

· October 2025: $19.3 billion, 16 times larger than the previous record

But the liquidation numbers tell only part of the story. The more interesting question concerns the mechanisms: how did external market events trigger this specific failure pattern? The answer reveals systemic weaknesses in the infrastructure of centralized trading platforms and the design of blockchain protocols.

Off-Chain Failures: Centralized Trading Platform Architecture

Infrastructure Overload and Rate Limiting

Trading platform APIs implement rate limiting to prevent abuse and manage server load. During normal operations, these limits allow legitimate trading while blocking potential attacks. During extreme volatility, when thousands of traders attempt to adjust positions simultaneously, these same rate limits become bottlenecks.

CEX limits liquidation notifications to one order per second, even while processing thousands of orders per second. During the October cascade, this created opacity. Users could not ascertain the real-time severity of the cascade. Third-party monitoring tools showed hundreds of liquidations per minute, while official data sources showed far fewer.

API rate limits prevented traders from modifying positions during the critical first hour, connection requests timed out, and order submissions failed. Stop-loss orders failed to execute, and position queries returned outdated data, turning this infrastructure bottleneck into an operational crisis.

Traditional trading platforms configure infrastructure with a safety margin for normal loads. However, normal loads are vastly different from stress loads, and average daily trading volume does not predict peak stress demands well. During the cascade, trading volume surged by 100 times or more, with position data queries increasing by 1000 times as each user checked their accounts simultaneously.

Figure 4.5: Impact of AWS Outage on Crypto Services

Auto-scaling cloud infrastructure helps, but cannot respond instantly; spinning up additional database read replicas takes minutes. Creating new API gateway instances also requires minutes. During those minutes, the margin system continues to mark position values based on corrupted price data from the overloaded order book.

Oracle Manipulation and Pricing Vulnerabilities

During the October cascade, a key design choice in the margin system became apparent: some trading platforms calculate collateral value based on internal spot market prices rather than external oracle data streams. Under normal market conditions, arbitrageurs maintain price consistency across different venues. But when infrastructure is under stress, this coupling can break.

Figure 3: Oracle Manipulation Flowchart

This interactive flowchart visualizes the five stages of the attack vector:

· Initial Sell-off: $60 million of sell pressure applied to USDe

· Price Manipulation: USDe plummets from $1.00 to $0.65 on a single exchange

· Oracle Failure: Margin system uses corrupted internal price data stream

· Cascade Trigger: Collateral is undervalued, forced liquidations begin

· Amplification: Total of $19.3 billion liquidated (amplified by 322 times)

This attack exploited Binance's use of spot market prices for wrapped synthetic collateral. When the attacker dumped $60 million of USDe into a relatively thin order book, the spot price plummeted from $1.00 to $0.65. The margin system, configured to mark collateral at spot prices, revalued all USDe-collateralized positions down by 35%. This triggered margin calls and forced liquidations for thousands of accounts.

These liquidations forced more sell orders into the same illiquid market, further driving down prices. The margin system observed these lower prices and marked the value of more positions, creating a feedback loop that amplified the $60 million sell pressure into $19.3 billion of forced liquidations.

Figure 4: Liquidation Cascade Feedback Loop

This feedback loop illustrates the self-reinforcing nature of the cascade:

Price drops → Triggers liquidations → Forced selling → Price drops further → [Repeat loop]

If a well-designed oracle system had been in place, this mechanism would not have worked. If Binance had used a time-weighted average price (TWAP) across multiple trading platforms, instantaneous price manipulation would not have affected collateral valuation. If they had used aggregated price data streams from Chainlink or other multi-source oracles, the attack would have failed.

The wBETH incident four days earlier showcased a similar vulnerability. wBETH was supposed to maintain a 1:1 exchange rate with ETH. During the cascade, liquidity dried up, and the wBETH/ETH spot market showed a 20% discount. The margin system correspondingly lowered the valuation of wBETH collateral, triggering liquidations of positions that were effectively fully collateralized by the underlying ETH.

Automatic Deleveraging (ADL) Mechanism

When liquidations cannot be executed at current market prices, trading platforms implement automatic deleveraging (ADL) to distribute losses among profitable traders. ADL forcibly closes profitable positions at current prices to cover the gap left by liquidated positions.

During the October cascade, Binance executed ADL across multiple trading pairs. Traders holding profitable long positions found their trades forcibly closed, not due to their own risk management failures, but because other traders' positions became insolvent.

ADL reflects a fundamental architectural choice in centralized derivatives trading. Trading platforms ensure they do not lose money. This means losses must be borne by one or more of the following:

· Insurance Fund (funds reserved by the trading platform to cover liquidation gaps)

· ADL (forced closure of profitable traders' positions)

· Socialized Losses (distributing losses among all users)

The size of the insurance fund relative to the size of open contracts determines the frequency of ADL. Binance's insurance fund totaled about $2 billion in October 2025. Relative to the $4 billion in open contracts for BTC, ETH, and BNB perpetual contracts, this provided a 50% coverage. However, during the October cascade, the total open contracts across all trading pairs exceeded $20 billion. The insurance fund could not cover the gap.

After the October cascade, Binance announced that when total open contracts remain below $4 billion, they guarantee that BTC, ETH, and BNB USDⓈ-M contracts will not experience ADL. This creates an incentive structure: trading platforms can maintain larger insurance funds to avoid ADL, but this occupies funds that could be deployed profitably.

On-Chain Failures: Limitations of Blockchain Protocols

The bar chart compares downtime across different events:

· Solana (February 2024): 5 hours - Voting throughput bottleneck

· Polygon (March 2024): 11 hours - Validator version mismatch

· Optimism (June 2024): 2.5 hours - Sequencer overload (airdrop)

· Solana (September 2024): 4.5 hours - Transaction spam attack

· Arbitrum (December 2024): 1.5 hours - RPC provider failure

Figure 5: Major Network Outages - Duration Analysis

Solana: Consensus Bottleneck

Solana experienced multiple outages during 2024-2025. The February 2024 outage lasted about 5 hours, while the September 2024 outage lasted 4-5 hours. These outages stemmed from similar root causes: the network could not handle transaction volumes during spam attacks or extreme activity.

Figure 5 details: Solana's outages (5 hours in February, 4.5 hours in September) highlight the recurring issue of network resilience under stress.

Solana's architecture is optimized for throughput. Under ideal conditions, the network processes 3,000-5,000 transactions per second with sub-second finality. This performance is several orders of magnitude higher than Ethereum. However, during stress events, this optimization creates vulnerabilities.

The September 2024 outage stemmed from a flood of spam transactions that overwhelmed the voting mechanism of validators. Solana validators must vote on blocks to reach consensus. During normal operations, validators prioritize voting transactions to ensure consensus progress. However, the protocol previously treated voting transactions the same as regular transactions in terms of fee markets.

When the transaction memory pool fills with millions of spam transactions, validators struggle to propagate voting transactions. Without sufficient votes, blocks cannot be finalized. Without finalized blocks, the chain stops. Users with pending transactions see them stuck in the memory pool. New transactions cannot be submitted.

StatusGator recorded multiple service outages for Solana during 2024-2025, while Solana never officially acknowledged them. This created information asymmetry. Users could not distinguish between local connection issues and network-wide problems. Third-party monitoring services provided accountability, but the platform should maintain a comprehensive status page.

Ethereum: Gas Fee Explosion

Ethereum experienced extreme gas fee spikes during the DeFi boom in 2021, with transaction fees for simple transfers exceeding $100. Complex smart contract interactions cost $500-$1,000. These fees rendered the network unusable for smaller transactions while enabling a different attack vector: MEV extraction.

Figure 7: Transaction Costs During Network Stress

This line graph dramatically shows the gas fee escalation across networks during stress events:

· Ethereum: $5 (normal) → $450 (peak congestion) - 90 times increase

· Arbitrum: $0.50 → $15 - 30 times increase

· Optimism: $0.30 → $12 - 40 times increase

The visualization indicates that even Layer 2 solutions experienced significant gas fee escalations, although starting from much lower levels.

Maximum Extractable Value (MEV) describes the profit that validators can extract by reordering, including, or excluding transactions. In high gas fee environments, MEV becomes particularly lucrative. Arbitrageurs race to front-run large DEX trades, and liquidation bots compete to be the first to liquidate under-collateralized positions. This competition manifests as gas fee bidding wars.

Users wanting to ensure their transactions are included during congestion must bid higher than MEV bots. This creates scenarios where transaction fees exceed transaction values. Want to claim your $100 airdrop? Pay $150 in gas fees. Need to add collateral to avoid liquidation? Compete with bots bidding $500 in priority fees.

Ethereum's gas limits restrict the total computational load per block. During congestion, users bid for scarce block space. The fee market works as designed: higher bidders gain priority. But this design makes the network increasingly expensive during high usage periods, precisely when users need access the most.

Layer 2 solutions attempt to address this issue by moving computation off-chain while inheriting Ethereum's security through periodic settlements. Optimism, Arbitrum, and other Rollups handle thousands of transactions off-chain and then submit compressed proofs to Ethereum. This architecture successfully reduces the cost per transaction during normal operations.

Layer 2: Sequencer Bottleneck

However, Layer 2 solutions introduce new bottlenecks. Optimism experienced an outage in June 2024 when 250,000 addresses simultaneously claimed airdrops. The sequencer, the component that orders transactions before submitting them to Ethereum, became overwhelmed, leaving users unable to submit transactions for several hours.

This outage illustrates that moving computation off-chain does not eliminate infrastructure demands. The sequencer must process incoming transactions, order them, execute them, and generate fraud proofs or ZK proofs for settlement with Ethereum. Under extreme traffic, the sequencer faces the same scalability challenges as independent blockchains.

The availability of multiple RPC providers must be maintained. If the primary provider fails, users should seamlessly failover to alternatives. During the Optimism outage, some RPC providers remained functional while others failed. Users whose wallets defaulted to the failing provider could not interact with the chain, even though the chain itself remained online.

AWS outages have repeatedly demonstrated the presence of centralized infrastructure risks in the crypto ecosystem:

· October 20, 2025 (today): Outage in the US East region affects Coinbase, as well as Venmo, Robinhood, and Chime. AWS acknowledges increased error rates in DynamoDB and EC2 services.

· April 2025: Regional outage simultaneously affects Binance, KuCoin, and MEXC. Multiple major exchanges became unavailable when their AWS-hosted components failed.

· December 2021: Outage in the US East region caused Coinbase, Binance.US, and "decentralized" trading platform dYdX to be paralyzed for 8-9 hours, while also affecting Amazon's own warehouses and major streaming services.

· March 2017: S3 outage prevented users from logging into Coinbase and GDAX for up to five hours, accompanied by widespread internet outages.

The pattern is clear: these trading platforms host critical components on AWS infrastructure. When AWS experiences regional outages, multiple major trading platforms and services become unavailable simultaneously. Users cannot access funds, execute trades, or modify positions during outages, precisely when market volatility may require immediate action.

Polygon: Consensus Version Mismatch

Polygon (formerly Matic) experienced an 11-hour outage in March 2024. The root cause involved a mismatch in validator versions, with some validators running old software versions while others ran upgraded versions. These versions computed state transitions differently.

Figure 5 details: The Polygon outage (11 hours) is the longest among the major events analyzed, highlighting the severity of consensus failures.

When validators arrive at different conclusions about the correct state, consensus fails, and the chain cannot produce new blocks because validators cannot agree on block validity. This creates a stalemate: validators running old software refuse to accept blocks produced by validators running new software, while validators running new software refuse to accept blocks produced by validators running old software.

Resolving this requires coordinating validator upgrades, but coordinating validator upgrades during an outage takes time. Each validator operator must be contacted, the correct software version must be deployed, and their validators must be restarted. In a decentralized network with hundreds of independent validators, this coordination can take hours or days.

Hard forks are typically triggered by block height triggers. All validators upgrade before a specific block height to ensure simultaneous activation, but this requires prior coordination. Incremental upgrades, where validators gradually adopt new versions, carry the risk of causing the exact version mismatches that led to the Polygon outage.

Architectural Trade-offs

Figure 6: Blockchain Trilemma - Decentralization vs Performance

This scatter plot visualizes the mapping of different systems across two key dimensions:

· Bitcoin: High decentralization, low performance

· Ethereum: High decentralization, medium performance

· Solana: Medium decentralization, high performance

· Binance (CEX): Minimal decentralization, maximum performance

· Arbitrum/Optimism: Medium-high decentralization, medium performance

Key Insight: No system can achieve maximum decentralization and maximum performance simultaneously; each design makes thoughtful trade-offs for different use cases.

Centralized trading platforms achieve low latency through architectural simplicity, with matching engines processing orders in microseconds and state residing in centralized databases. No consensus protocol introduces overhead, but this simplicity creates single points of failure, and cascading failures propagate through tightly coupled systems when infrastructure is under stress.

Decentralized protocols distribute state across validators, eliminating single points of failure. High-throughput chains maintain this property during outages (no loss of funds, only temporary loss of activity). However, achieving consensus among distributed validators introduces computational overhead; validators must agree before state transitions are finalized. When validators run incompatible versions or face overwhelming traffic, the consensus process may temporarily halt.

Adding replicas increases fault tolerance but raises coordination costs. In Byzantine fault-tolerant systems, each additional validator increases communication overhead. High-throughput architectures minimize this overhead through optimized validator communication, achieving superior performance but becoming vulnerable to certain attack patterns. Security-focused architectures prioritize validator diversity and consensus robustness, limiting throughput at the base layer while maximizing resilience.

Layer 2 solutions attempt to provide both properties through layered designs. They inherit Ethereum's security properties through L1 settlements while providing high throughput through off-chain computation. However, they introduce new bottlenecks at the sequencer and RPC layers, indicating that architectural complexity creates new failure modes while solving some problems.

Scalability Remains a Fundamental Issue

These events reveal a consistent pattern: systems are provisioned for normal loads and then catastrophically fail under stress. Solana effectively handles routine traffic but collapses when transaction volumes increase by 10,000%. Ethereum gas fees remain reasonable until DeFi adoption triggers congestion. Optimism's infrastructure runs well until 250,000 addresses simultaneously claim airdrops. Binance's API functions normally during regular trading but becomes constrained during liquidation cascades.

The October 2025 event showcased this dynamic at the exchange level. During normal operations, Binance's API rate limits and database connections were sufficient, but during the liquidation cascade, these limits became bottlenecks as each trader attempted to adjust positions simultaneously. The margin system, designed to protect exchanges through forced liquidations, amplified the crisis by creating forced sellers at the worst possible moment.

Auto-scaling provides insufficient protection against step-function increases in load. Spinning up additional servers takes minutes, during which the margin system marks position values based on corrupted price data from the thin order book, and by the time new capacity comes online, the cascade has already propagated.

Over-provisioning resources for rare stress events consumes funds during normal operations. Exchange operators optimize for typical loads, accepting occasional failures as an economically rational choice. The costs of downtime are externalized to users, who experience liquidations, trades stuck, or inaccessible funds during critical market movements.

Infrastructure Improvements

Figure 8: Distribution of Infrastructure Failure Modes (2024-2025)

A pie chart breakdown of root causes shows:

· Infrastructure Overload: 35% (most common)

· Network Congestion: 20%

· Consensus Failures: 18%

· Oracle Manipulation: 12%

· Validator Issues: 10%

· Smart Contract Vulnerabilities: 5%

Several architectural changes can reduce the frequency and severity of failures, though each involves trade-offs:

Separation of Pricing and Liquidation Systems

The issues in October partly stemmed from coupling margin calculations with spot market prices. Using exchange rates for wrapped assets instead of spot prices could have avoided the mispricing of wBETH. More generally, critical risk management systems should not rely on potentially manipulable market data. Independent oracle systems with multi-source aggregation and TWAP calculations provide more robust price data streams.

Over-Provisioning and Redundant Infrastructure

The AWS outage in April 2025 that impacted Binance, KuCoin, and MEXC demonstrated the risks of centralized infrastructure reliance. Running critical components across multiple cloud providers increases operational complexity and costs but eliminates correlated failures. Layer 2 networks can maintain multiple RPC providers with automatic failover. The additional costs may seem wasteful during normal operations but prevent hours of downtime during peak demand.

Enhanced Stress Testing and Capacity Planning

The pattern of systems running well under normal conditions until failure indicates insufficient testing under stress. Simulating 100 times normal load should be standard practice, as identifying bottlenecks during development is cheaper than discovering them during actual outages. However, realistic load testing remains challenging. Production traffic exhibits patterns that synthetic tests cannot fully capture, and user behavior during actual crashes differs from testing periods.

The Path Forward

Over-provisioning offers the most reliable solution but conflicts with economic incentives. Maintaining 10 times excess capacity for rare events costs money daily to prevent issues that may occur once a year. Until catastrophic failures impose enough costs to justify over-provisioning, systems will continue to fail under stress.

Regulatory pressure may force change. If regulations mandate 99.9% uptime or limit acceptable downtime, trading platforms will need to over-provision. However, regulations typically follow disasters rather than prevent them. The collapse of Mt. Gox in 2014 led Japan to establish formal regulations for cryptocurrency exchanges. The October 2025 liquidation cascade is likely to trigger similar regulatory responses. Whether these responses specify outcomes (maximum acceptable downtime, maximum slippage during liquidations) or implementation methods (specific oracle providers, circuit breaker thresholds) remains uncertain.

The fundamental challenge is that these systems operate continuously in global markets but rely on infrastructure designed for traditional business hours. When stress occurs at 02:00, teams scramble to deploy fixes while users face escalating losses. Traditional markets halt trading during stress; crypto markets simply crash. Whether this represents a feature or a flaw depends on perspective and position.

Blockchain systems have achieved significant technical complexity in a short time. Maintaining distributed consensus across thousands of nodes represents a genuine engineering achievement. However, achieving reliability under stress requires moving beyond prototype architectures to production-grade infrastructure. This shift requires funding and prioritizing robustness over the speed of feature development.

The challenge lies in how to prioritize robustness over growth during bull markets when everyone is making money and downtime seems to be someone else's problem. By the time the next cycle stress-tests the system, new vulnerabilities will emerge. Whether the industry learns from the October 2025 event or repeats similar patterns remains an open question. History suggests we will discover the next critical vulnerability through another multi-billion-dollar failure under stress.

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