Your leveraged position almost wiped out your capital last night. The real culprit isn't market volatility but an invisible killer lurking behind the DeFi system.

We've seen many stories like this: the market is very calm, with no unusual fluctuations, yet the lending protocols or derivative positions you bet on suddenly get liquidated in a chain reaction. Your account is emptied in an instant, and by the time you react, it's too late.

Digging deeper, you'll find that the main culprit is often a problem with the oracle price feeds. A single incorrect data point can trigger a chain reaction across the entire chain. This isn't a low-probability event; it's a recurring problem in the DeFi space.

Where is the root cause? Essentially, blockchains can't directly connect to real-world data. You need a "bridge"—an oracle—to bring external information like prices, weather, and exchange rates onto the chain. The problem is, this bridge is too easy to collapse:

- Data may be delayed or tampered with
- An extreme price spike can instantly trigger a liquidation storm
- Centralized nodes may act maliciously, or a single point of failure can paralyze the entire system

Some have thought of an alternative approach. Instead of performing all calculations on-chain (which is slow and expensive), data collection and complex computations are done off-chain, and only the results are submitted on-chain for verification and confirmation. It sounds like doing "judicial rulings"—evidence collection and analysis are completed outside the courtroom, and the judge just makes a final decision based on sufficient evidence.

This layered verification approach has several advantages: the computational logic can be flexibly customized, and security isn't compromised. Derivative protocols may require complex volatility calculations, insurance protocols may need weather data—each scenario has different requirements, but all can be handled within this framework.

Whether this approach can fully solve the oracle problem remains to be seen through practical application. But at least it points in a direction: instead of passively bearing risks, it's better to proactively design more resilient data architectures.