Do you remember the oracle disaster in 2022? A major lending protocol was drained of tens of millions of dollars in a flash loan attack. Sounds crazy, but it was actually due to a fatal vulnerability: its price source was too "obedient."

Traditional oracles are quite crude—just pull data from a few sources and take the median or average. Where's the problem? The weights and data sources are static. Attackers only need to compromise a few high-weight sources, and the entire system is led around by the nose.

That's why the new generation of oracles is starting to incorporate "reputation scoring." Instead of treating all data sources equally, better-performing sources gain more influence.

APRO's approach is this: each data node in the network receives a real-time dynamic reputation score. How is this score calculated?

First, look at historical accuracy—how consistent your provided data has been with other trusted sources. Next, responsiveness and stability—can you hold up under network stress or adverse conditions? Then, anomaly detection—if your data deviates too much from others, the system will flag you and reduce your weight.

The key is, each time data is fetched, APRO doesn't treat all sources equally. It dynamically weights them based on their reputation scores at that moment. Nodes that have just provided outlier data? Their weight is immediately cut, and they may even be kicked out in this round. This significantly raises the cost for attackers to manipulate the system.