登录页一般都要用图形验证码来防止机器人，在 Node.js 中也有很多生成图形验证码的库，其中 svg-captcha 的 star 比较多，官方号称它有以下优点：
- 轻量级，无需安装 C++ 图形库依赖
- 生成矢量 svg 验证码，图片放大不失真
- 比普通图片验证码要更难识别
于是我们的一个商业项目就用了这个库，结果踩雷了，被竞争对手用程序攻击.....，说多了都是泪，因为破解方法太简单，100% 秒识别，感兴趣的可以查看完整的破解代码，在此也提醒一下大家：
svg-captcha 有严重漏洞，比普通验证码更容易识别，千万不要用
破解原理
正常来讲，图形验证码的破解是用机器学习的办法识别图形里面的文字，还是有点技术含量的，svg-captcha 弱智的地方在于，用穷举法就暴力破解了...
svg-captcha 生成验证码的代码如下：
const svgCaptcha = require('svg-captcha')
const options = {
size: 4,
ignoreChars: '0o1i',
noise: 0,
color: false,
}
const svg = svgCaptcha.create(options)
console.log(svg)
会得到如下的输出：
{
text: '4Bay',
data: '<svg xmlns="http://www.w3.org/2000/svg" width="150" height="50" viewBox="0,0,150,50"><path fill="#333" d="M84.98 28.87L85.01 28.90L85.04 28.93Q82.41 28.85 81.38 30.03L81.46 30.11L81.51 30.16Q80.52 31.37 80.52 34.00L80.47 33.96L80.47 33.96Q80.33 38.92 84.94 38.73L85.04 38.83L85.07 38.86Q87.11 39.00 88.46 37.43L88.40 37.37L88.41 37.38Q89.82 35.88 89.59 33.82L89.53 33.76L89.60 33.84Q89.59 31.27 88.71 30.24L88.78 30.31L88.72 30.25Q87.20 28.88 84.99 28.88ZM89.99 38.64L89.99 38.64L90.14 38.79Q88.94 41.25 84.26 41.25L84.12 41.10L82.77 41.20L82.78 41.21Q80.34 41.13 79.24 39.99L79.33 40.08L79.23 39.98Q78.25 39.00 77.98 36.61L78.06 36.68L78.02 36.64Q77.81 35.10 77.74 33.31L77.68 33.25L77.72 33.30Q77.72 32.34 77.57 30.40L77.51 30.35L77.50 30.34Q77.45 26.10 82.63 26.10L82.63 26.10L84.11 26.02L84.10 26.01Q87.16 26.18 89.26 27.48L89.21 27.42L89.13 27.35Q90.06 27.97 90.56 28.66L90.39 28.50L90.47 28.57Q90.75 27.78 91.28 26.15L91.20 26.06L91.32 26.19Q92.52 25.98 94.85 25.37L94.87 25.40L94.89 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}
其中：
- text 字段是验证码里面的文字，需要保存到后端
- data 字段是 svg 内容，需要返回给前端
仔细观察一下 data 里面的内容，如果有 4 个字母的话，会出现 4 个 <path fill="#444" d="....，由于数据集是 a-zA-z0-9，于是破解作者就写了一个脚本统计 svg 里面不同字母或数字对应的 path 的长度，跑了几十万次之后，得到了如下结果：
{
'0': { '2382': 10819, '2580': 3769 },
'1': { '998': 10861, '1081': 3621 },
'2': { '2546': 11012, '2758': 3676 },
'3': { '3878': 10913, '4201': 3599 },
'4': { '2140': 10985, '2318': 3674 },
'5': { '2606': 10925, '2823': 3623 },
'6': { '2632': 10834, '2851': 3615 },
'7': { '2042': 10956, '2212': 3636 },
'8': { '3414': 10742, '3698': 3638 },
'9': { '2800': 5343, '3033': 1811 },
A: { '1840': 3618, '1844': 1825, '1993': 1885 },
B: { '3054': 11034, '3308': 3710 },
C: { '2198': 8820, '2199': 1536, '2200': 538, '2201': 92, '2202': 4, '2381': 3579 },
D: { '1996': 10745, '2162': 3651 },
...
x: { '1610': 7350, '1613': 3706, '1744': 3762 },
y: { '1274': 10830, '1380': 3490 },
z: { '1694': 11224, '1835': 3701 }
}
也就是说，每个字母的 path 长度，也就那几种，根据这个特点，只要知道 path 的长度，就能反推验证码里面是什么字母了。
后来，破解作者就给原仓库提了个 issue，希望能够解决这个 bug，但是大半年都过去了，依然没有动静，感觉这个库不维护了。
修复补丁
一个开源贡献者实在看不下去了，就手动修复了这个问题，提了 PR，内容如下：
随机拆分直线(L)与二次贝塞尔曲线(Q)，使得每个字母生成的向量每次均不同。
潜在的问题或改进方向：
生成的效率会降低，并且生成出来的 SVG 大小会变大。由于拆分也会导致生成图案变丑。 基于概率的拆分，或许会被基于统计的方式攻破。或许不应该将概率做成配置项，而是应该完全随机。 拆分所生成的点可能有特征。 每 L 或 Q 只会被拆分一次，可能应该改成有一定概率多次拆分。 由于拆分导致线条不连贯，如果逐点扰动对图案质量有显著影响（但是我不觉得对 SVG 图像进行逐点扰动有什么价值。同样的，噪点与横贯线条在 SVG 里也易于识别）。
但是原作者就是不 merge，后来这位贡献者就自己上传到 npm 了，包名是 svg-captcha-fixed，如果大家还是想使用 svg 图形验证码的话，建议用这个包。
