x航 mf登录逆向-171主机测评

技术文章大纲：X航MF登录逆向分析

网址aHR0cHM6Ly93d3cueGlhbWVuYWlyLmNvbS96aC1jbi8=

登录流程抓包与协议分析

1，使用模拟数据去请求登录，观察接口。登录接口走的是verify。里面的参数有account,deviceId,keyPairid,pageToken,password,smDeviceId

打上断点，重新触发，发现cookie设置了一个shumeiBlockBox

继续跟栈，发现smDeviceId是smsdk生成的。deviceId是从缓存读取，keyPairId读取缓存值

字段解密

deviceId,keyPairId字段：全局搜索deviceId发现这两个值是请求了getPublicKey返回的值

account,password字段：进入加密方法，可以发现是RSA加密，秘钥就是getPublicKey返回的key

deviced字段：全局搜索localStorage.xmair_fingerprint,发现是指纹

跟栈，调用了一个方法，传入环境来使用。

扣代码，这个扣下来就能用，把环境copy一下

U = function (e) {
throw new Error(
'\\'new Fingerprint()\\' is deprecated, see https://github.com/Valve/fingerprintjs2#upgrade-guide-from-182-to-200'
)
};

l = function (e, t) {
t = t ||
0;
for (
var a = (e = e || '').length % 16,
n = e.length – a,
r = [
0,
t
],
i = [
0,
t
],
o = [
0,
0
],
l = [
0,
0
],
s = [
2277735313,
289559509
],
c = [
1291169091,
658871167
],
u = 0;
u < n;
u += 16
) o = [
255 & e.charCodeAt(u + 4) | (255 & e.charCodeAt(u + 5)) << 8 | (255 & e.charCodeAt(u + 6)) << 16 | (255 & e.charCodeAt(u + 7)) << 24,
255 & e.charCodeAt(u) | (255 & e.charCodeAt(u + 1)) << 8 | (255 & e.charCodeAt(u + 2)) << 16 | (255 & e.charCodeAt(u + 3)) << 24
],
l = [
255 & e.charCodeAt(u + 12) | (255 & e.charCodeAt(u + 13)) << 8 | (255 & e.charCodeAt(u + 14)) << 16 | (255 & e.charCodeAt(u + 15)) << 24,
255 & e.charCodeAt(u + 8) | (255 & e.charCodeAt(u + 9)) << 8 | (255 & e.charCodeAt(u + 10)) << 16 | (255 & e.charCodeAt(u + 11)) << 24
],
o = g(o, s),
o = f(o, 31),
o = g(o, c),
r = m(r, o),
r = f(r, 27),
r = d(r, i),
r = d(g(r, [
0,
5
]), [
0,
1390208809
]),
l = g(l, c),
l = f(l, 33),
l = g(l, s),
i = m(i, l),
i = f(i, 31),
i = d(i, r),
i = d(g(i, [
0,
5
]), [
0,
944331445
]);
switch (o = [
0,
0
], l = [
0,
0
], a) {
case 15:
l = m(l, h([0,
e.charCodeAt(u + 14)], 48));
case 14:
l = m(l, h([0,
e.charCodeAt(u + 13)], 40));
case 13:
l = m(l, h([0,
e.charCodeAt(u + 12)], 32));
case 12:
l = m(l, h([0,
e.charCodeAt(u + 11)], 24));
case 11:
l = m(l, h([0,
e.charCodeAt(u + 10)], 16));
case 10:
l = m(l, h([0,
e.charCodeAt(u + 9)], 8));
case 9:
l = m(l, [
0,
e.charCodeAt(u + 8)
]),
l = g(l, c),
l = f(l, 33),
l = g(l, s),
i = m(i, l);
case 8:
o = m(o, h([0,
e.charCodeAt(u + 7)], 56));
case 7:
o = m(o, h([0,
e.charCodeAt(u + 6)], 48));
case 6:
o = m(o, h([0,
e.charCodeAt(u + 5)], 40));
case 5:
o = m(o, h([0,
e.charCodeAt(u + 4)], 32));
case 4:
o = m(o, h([0,
e.charCodeAt(u + 3)], 24));
case 3:
o = m(o, h([0,
e.charCodeAt(u + 2)], 16));
case 2:
o = m(o, h([0,
e.charCodeAt(u + 1)], 8));
case 1:
o = m(o, [
0,
e.charCodeAt(u)
]),
o = g(o, s),
o = f(o, 31),
o = g(o, c),
r = m(r, o)
}
return r = m(r, [
0,
e.length
]),
i = m(i, [
0,
e.length
])
r = d(r, i),
i = d(i, r),
r = T(r),
i = T(i),
r = d(r, i),
i = d(i, r),
('00000000' + (r[0] >>> 0).toString(16)).slice( – 8) + ('00000000' + (r[1] >>> 0).toString(16)).slice( – 8) + ('00000000' + (i[0] >>> 0).toString(16)).slice( – 8) + ('00000000' + (i[1] >>> 0).toString(16)).slice( – 8)
},

time_s=new Date().getTime();
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aliased line width range:[1, 1],webgl aliased point size range:[1, 1024],webgl alpha bits:8,webgl antialiasing:yes,webgl blue bits:8,webgl depth bits:24,webgl green bits:8,webgl max anisotropy:16,webgl max combined texture image units:32,webgl max cube map texture size:16384,webgl max fragment uniform vectors:1024,webgl max render buffer size:16384,webgl max texture image units:16,webgl max texture size:16384,webgl max varying vectors:30,webgl max vertex attribs:16,webgl max vertex texture image units:16,webgl max vertex uniform vectors:4096,webgl max viewport dims:[32767, 32767],webgl red bits:8,webgl renderer:ANGLE (Intel, Intel(R) HD Graphics Direct3D11 vs_5_0 ps_5_0), or similar,webgl shading language version:WebGL GLSL ES 1.0,webgl stencil bits:0,webgl vendor:Mozilla,webgl version:WebGL 1.0,webgl unmasked vendor:Google Inc. (Intel),webgl unmasked renderer:ANGLE (Intel, Intel(R) HD Graphics Direct3D11 vs_5_0 ps_5_0), or similar,webgl vertex shader high float precision:23,webgl vertex shader high float precision rangeMin:127,webgl vertex shader high float precision rangeMax:127,webgl vertex shader medium float precision:23,webgl vertex shader medium float precision rangeMin:127,webgl vertex shader medium float precision rangeMax:127,webgl vertex shader low float precision:23,webgl vertex shader low float precision rangeMin:127,webgl vertex shader low float precision rangeMax:127,webgl fragment shader high float precision:23,webgl fragment shader high float precision rangeMin:127,webgl fragment shader high float precision rangeMax:127,webgl fragment shader medium float precision:23,webgl fragment shader medium float precision rangeMin:127,webgl fragment shader medium float precision rangeMax:127,webgl fragment shader low float precision:23,webgl fragment shader low float precision rangeMin:127,webgl fragment shader low float precision rangeMax:127,webgl vertex shader high int precision:0,webgl vertex shader high int precision rangeMin:31,webgl vertex shader high int precision rangeMax:30,webgl vertex shader medium int precision:0,webgl vertex shader medium int precision rangeMin:31,webgl vertex shader medium int precision rangeMax:30,webgl vertex shader low int precision:0,webgl vertex shader low int precision rangeMin:31,webgl vertex shader low int precision rangeMax:30,webgl fragment shader high int precision:0,webgl fragment shader high int precision rangeMin:31,webgl fragment shader high int precision rangeMax:30,webgl fragment shader medium int precision:0,webgl fragment shader medium int precision rangeMin:31,webgl fragment shader medium int precision rangeMax:30,webgl fragment shader low int precision:0,webgl fragment shader low int precision rangeMin:31,webgl fragment shader low int precision rangeMax:30Google Inc. (Intel)~ANGLE (Intel, Intel(R) HD Graphics Direct3D11 vs_5_0 ps_5_0), or similartruefalsefalsefalsefalse0,false,falseswf object not loaded35.749972093850374'+time_s
s= l(a,31)
console.log(s)

function getDevoceId(){

return s
}

payToken:页面元素的值，写一个正则提取