Why is DeFi always being hacked? You might be ignoring "these red flags"

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In 2024, losses in the DeFi sector reached as high as $1.029 billion, with $649 million lost in 2025, and $137 million stolen in the first quarter of 2026 alone. PANews Note: Since April 2026, DeFi losses have exceeded $600 million. Why do hacker attacks occur repeatedly? Why do we always ignore the same danger signals? When you strip away the surface of these vulnerabilities, you will find that they are by no means random, but follow a traceable pattern. This article organizes the patterns behind major DeFi security incidents and the early warning signals that are easily overlooked. Based on a deep review of over a hundred attack cases, the article provides core prevention suggestions at the end. ### The Truth and Classification of Frequent Vulnerabilities Before we begin, it is necessary to clarify why we divide these vulnerabilities into different categories. Fundamentally, DeFi failures often occur at specific layers of the system, and the way each layer collapses is essentially different: - **Code Level:** Fails when assumptions are not enforced. There are no obvious logical errors, but edge cases, constraints, or invariants have never been fully checked. - **Infrastructure:** Fails when trust is placed in systems that can be compromised. - **Business Logic:** Fails when "playing by the rules" itself becomes a means of attack. The following is a structured summary of typical cases for each type of vulnerability: #### 1. Infrastructure: Correct Control, Wrong Context Infrastructure fails when power is used but lacks comprehensive awareness (rather than when keys are stolen). In various security incidents, we always see a consistent pattern: the right person signed the transaction, used the correct permissions, and the system operated exactly as designed. However, funds were still lost. Because the system validates authenticity, not intent. A valid signature only proves who signed it, but not that they truly understood what they were signing. This gap between validation and understanding is the breeding ground for infrastructure collapse. * **@DriftProtocol:** They signed too early. The transaction was valid, and the signature was authentic. They just didn't expect it to be used later. It was approved during a routine check when nothing was happening. Then one day, it was executed. Nothing was forged or altered. The problem is simple: they signed something but didn't control when it would be used. * **@Bybit_Official:** They signed the wrong thing. The system worked normally, and the signature was valid. People just signed something different from what they imagined. They saw a normal transfer and approved it. But at the underlying level, it was changing the control of the wallet. In the usual sense, nothing was hacked; everything followed the rules. The problem is simple: what they saw... was not what they signed. * **@UXLINKofficial:** They had the right to do so. The system allowed it, and the permissions were valid. No keys were stolen, and no checks were bypassed. The admin role was changed, and ownership was reassigned. All of this was done through legitimate calls, and everything ran as designed. The problem is simple: the system granted enough power and trusted that it would not be abused. #### 2. Code: Where Assumptions Are Not Enforced Code vulnerabilities do not come from obvious bugs. They often come from systems that work as expected, just not under all conditions. - Rules exist but are not enforced everywhere; - Edge cases are ignored until they are maliciously triggered; - Mathematical formulas work in theory but collapse during code implementation; - Security checks cover the expected path but not the actual attack path. In short, code fails where its assumptions no longer hold. * **Bunni:** The math was fine, until it wasn't. The system was audited, and they confirmed the code was correct. The model made perfect sense on paper: liquidity, pricing, everything checked out. But in practice, tiny rounding errors appeared. And they didn't offset; they accumulated. The attacker didn't break the system; they just repeated it: over and over again. The problem is simple: the mathematical theory is correct, but the code implementation is not precise. * **@Balancer:** Small errors, repeated. The system worked normally, and the math was correct. Each transaction had a tiny rounding loss, almost negligible. But it didn't reset; it accumulated. The attacker didn't just exploit it once; they exploited it multiple times in one flow. The problem is simple: if repeated enough times, a small error snowballs into a big one. * **Venus:** Rules exist, just not everywhere. The system had a limit, and the check mechanism was implemented. But only in one place. Through another path

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