“氛围代码”耻辱墙
The "Vibe Coding" Wall of Shame

原始链接: https://crackr.dev/vibe-coding-failures

这些失败的根本原因相同:代码是由不理解代码的人发布的。人工智能生成了一些看似正确的东西,通过了初步检查,然后进入生产环境。结果是数据库暴露、订单丢失以及无需任何用户交互即可利用的漏洞。这种模式正在加速。2026年1月,归因于人工智能生成代码的CVE条目数量为6个,到3月已增加到35个以上。Tenzai的一项研究发现,由5个主要人工智能编码工具构建的15个应用程序中存在69个漏洞。每个应用程序都缺乏CSRF保护。每种工具都引入了SSRF漏洞。解决办法与以往一样:理解你的代码。数据结构、算法、系统设计以及推理软件实际执行操作的能力。人工智能在由理解其输出的人使用时是一种强大的工具。如果没有这种理解,它将成为一种负担。

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原文

These failures share a common root cause: code was shipped by people who did not understand it. AI generated something that looked correct, passed a cursory check, and went to production. The result was exposed databases, lost orders, and vulnerabilities that required zero user interaction to exploit.

The pattern is accelerating. CVE entries attributed to AI-generated code jumped from 6 in January 2026 to 35+ in March. A Tenzai study found 69 vulnerabilities across 15 apps built by 5 major AI coding tools. Every single app lacked CSRF protection. Every tool introduced SSRF vulnerabilities.

The antidote is the same as it has always been: understand your code. Data structures, algorithms, system design, and the ability to reason about what software is actually doing. AI is a powerful tool when wielded by someone who understands the output. Without that understanding, it is a liability.

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