Author: Karl Marx OnChain

Compiled by: Yuliya, PANews

The DeFi sector suffered losses of $1.029 billion in 2024, $649 million in 2025, and $137 million was stolen in the first quarter of 2026 alone. PANews note: DeFi losses have exceeded $600 million since April 2026.

Why do hacking attacks keep happening? Why do we always ignore the same warning signs? Once you peel back the layers of these vulnerabilities, you'll find that they don't happen randomly, but rather they follow a pattern.

This article analyzes the underlying patterns and easily overlooked early warning signs behind major DeFi security incidents. Based on an in-depth review of over a hundred attack cases, the article provides core prevention recommendations at the end.

The truth and classification of frequent vulnerabilities

Before we begin, it is necessary to clarify why we are classifying these vulnerabilities into different categories.

Fundamentally, DeFi failures often occur at specific layers of the system, and the ways in which each layer collapses are fundamentally different:

• At the code level: It fails when the assumptions are not enforced. There is no obvious logical error, but edge cases, constraints, or invariants are never fully checked.

• Infrastructure: It fails when trust is placed in a potentially compromised system.

• Business logic: It will fail when "playing by the rules" itself becomes a means of attack.

The following is a structured summary of typical vulnerability cases:

1. Infrastructure: The control is correct, but the context is wrong.

Infrastructure fails when power is used without full awareness (not when keys are stolen). A consistent pattern emerges across security incidents: the right people sign transactions, the right permissions are used, and the system functions exactly as designed.

However, the funds were still lost. This is because the system verifies authenticity, not intent. A valid signature only proves who signed it, but not that they truly understood what they signed. This gap between verification and understanding is the breeding ground for infrastructure collapse.

@DriftProtocol: They signed off too early.

The transaction was valid, and the signature was genuine. They just didn't expect it would be used later.

It was approved during a routine inspection, and nothing happened at the time. Then one day it was executed. Nothing was forged or altered.

The problem is simple: they signed off on something, but didn't control when it was used.

@Bybit_Official: They signed the wrong document.

The system is working properly, and the signatures are valid. People are just signing things that are different from what they expected.

What they saw was a normal transfer, and they approved it. But at the bottom, it was changing control of the wallet. In the conventional sense, nothing was hacked, and everything followed the rules.

The problem is simple: what they saw... was not what they signed.

@UXLINKofficial: They have the right to do so.

The system allows this, and the permissions are valid. No key was stolen, and no checks were bypassed.

The administrator role was changed, and ownership was redistributed. All of this was accomplished through legitimate calls, and everything functioned as designed.

The problem is simple: the system has been given enough power and there is confidence that it will not be abused.

2. Code: Assuming that certain parts are not enforced.

Code vulnerabilities don't come from obvious bugs. They often come from systems that work as expected, but not under all conditions.

• The rules exist, but they are not enforced everywhere;

• Edge cases are ignored until they are maliciously triggered;

• The mathematical formula works in theory, but it crashes when implemented in code.

• The security check covered the expected paths, but not the actual attack paths.

In short, the code failed where its assumptions no longer hold true.

Bunni: The mathematical principles are sound until they go wrong.

The system was audited, and they confirmed the code was correct. The model made perfect sense on paper: liquidity, pricing—everything was verified to be correct.

In practice, however, tiny rounding errors occurred. And instead of canceling each other out, these errors accumulated. The attacker didn't break the system; they simply repeated it over and over again. The problem is simple: the mathematical theory is correct, but the code implementation is imprecise.

@Balancer: Minor error, keeps recurring.

The system is functioning correctly, and the mathematical calculations are accurate. Each transaction incurs a negligible rounding loss.

But instead of resetting, it accumulated. The attackers didn't just exploit it once; they used it multiple times within a single process.

The problem is simple: if repeated enough times, a small mistake will snowball into a big mistake.

Venus: Rules exist, but they are not omnipresent.

The system has a limitation: a checking mechanism has been implemented, but only in one place.

The same rule doesn't apply through the alternative path. The attackers didn't bypass the system; they simply bypassed that checkpoint. The problem is simple: a rule that isn't enforced everywhere is equivalent to no rule at all.

3. Business Logic: When the system trusts the wrong thing...

The system follows its own rules; it trusts the price. If the price rises, you can borrow more money.

Therefore, the attackers buy their own assets, artificially inflating prices. Now the system believes they are wealthy and allows them to borrow real, valuable assets. Then they cash out and leave. Nothing is broken; the system simply trusts something extremely easy to manipulate.

Mango: He made himself look very wealthy.

System trust pricing: Higher prices → More collateral → More loans.

The attackers purchased a large amount of illiquid tokens, causing the price to rise. They then used this inflated price to borrow real assets from the protocol. Afterward, they stopped supporting the price, causing it to crash. The collateral was no longer sufficient, but the borrowed funds had vanished. Nothing was actually hacked; the system simply believed in a manipulated price.

Impermax: Prices are too easily shaken.

The system trusts the price, but the market is too weak.

An attacker borrows tokens and then trades against a low-liquidity pool. The price fluctuates wildly, beyond what is expected. The system then deems the position unsafe and forces a liquidation. The attacker had orchestrated this and pocketed the profits. Nothing was broken; the price was simply too easily manipulated.

Ultimate Lesson: How to Survive and Thrive?

Of all these vulnerabilities, one thing is clear: you don't need to make a mistake to lose money; you only need to be exposed to risk when the system crashes.

The problem isn't that the protocols are broken, but that they rely on things that can go wrong: assumptions, context, or price. Therefore, our goal isn't to find perfect systems, but to control how much you trust them.

In practice, this means:

• Don't blindly trust what you see on the interface.

• Don't assume that the rules will always protect you.

• Do not treat prices or yields as absolute truths.

• Avoid exposing all your assets in one place, because when failures occur, they often happen very quickly and without warning.

Survival boils down to limiting damage: using systems that can enforce constraints, avoiding settings that are easily manipulated, and always retaining the ability to exit.

In DeFi, you can't win by trusting the system; you can only survive by controlling how much it can hurt you.