Python还原CryptoJs_DES_CBC模式

Python_CryptoJs_js逆向破解

一、前言
- 1、Redeme
二、对称加密解密DES
- 1、对称加密与非对称加密
- 2、DES对称加密介绍
- 3、实现DES的4种模式
- 4、关于补位PKCS7和PKCS5区别
三、网站案例分析
四、通过Python还原CryptoJS
- 1、DES_CBC模式参数
- 2、Python将CryptoJS的DES还原

一、前言

1、Redeme

如果对DES加密解密了解的话，可以跳过目录二，直接看目录三目录四实例操作；
简单的DES案例可以看这篇文章
本博文主要是对js里面CryptoJS进行逆向，并用python进行还原实现，只用于js逆向交流学习

二、对称加密解密DES

1、对称加密与非对称加密

对称加密：加密和解密时使用同一个密钥，加密速度非常快，适合经常发送数据的场合；缺点是密钥的传输比较麻烦；

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加密_密钥K

解密_密钥K

明文

密文

非对称加密：加密和解密时使用不同密钥，是用数学上的难解问题构造的，通常加密解密的速度比较慢，适合偶尔发送数据的场合，优点是密钥传输方便；常见的非对称加密算法为RSA、ECC和EIGamal；

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加密_密钥A _公钥

解密_密钥B_私钥

明文

密文

2、DES对称加密介绍

Des对称加密，是一种比较传统的加密方式，其加密运算、解密运算使用的是同样的密钥，信息的发送者和信息的接收者在进行信息的传输与处理时，必须共同持有该密码（称为对称密码），是一种对称加密算法；
DES只是个基本算法，实现DES有几种模式，主要有ECB、CBC、CFB、OFB：

3、实现DES的4种模式

ECB模式（电子密码本模式：Electronic codebook）：ECB是最简单的块密码加密模式，加密前根据加密块大小（如DES为64位）分成若干块，之后将每块使用相同的密钥单独加密，解密同理
CBC模式（密码分组链接：Cipher-block chaining）：CBC模式对于每个待加密的密码块在加密前会先与前一个密码块的密文异或然后再用加密器加密。第一个明文块与一个叫初始化向量的数据块异或
CFB模式（密文反馈：Cipher feedback）：与ECB和CBC模式只能够加密块数据不同，CFB能够将块密文（Block Cipher）转换为流密文（Stream Cipher）
OFB模式（输出反馈：Output feedback）：OFB是先用块加密器生成密钥流（Keystream），然后再将密钥流与明文流异或得到密文流，解密是先用块加密器生成密钥流，再将密钥流与密文流异或得到明文，由于异或操作的对称性所以加密和解密的流程是完全一样的

4、关于补位PKCS7和PKCS5区别

PKCS7Padding和PKCS5Padding实际只是协议不一样
根据相关资料说明：PKCS5Padding明确定义了加密块是8字节，PKCS7Padding加密快可以是1-255之间
但是封装的DES算法默认都是8字节，所以可以认为PKCS7和PKCS5一样
数据补位实际是在数据不满8字节的倍数，才补充到8字节的倍数的填充过程

三、网站案例分析

具体分析暂不介绍，分析网址为空气质量，类似这个分析空气质量js逆向里面有相关DES算法
坑位：一年前看了空气质量逆向解析的文章，写着要反混淆，因为没有自己实操；觉得完全可以通过调试找到还原后VM里面的js真实脚本，或者是通过调用栈定位调试到VM里面的js脚本；
排坑：直到最近才发现，还是要去根据看网页network里面的这个混淆的js脚本https://www.aqistudy.cn/js/encrypt_eQI0SRlwyF5D.min.js ，将其反混淆，再去分析，
原因：为啥呢？因为该网站的加密解密js脚本是动态变化的，所以你必须拿到每次响应的混淆js脚本，将其还原，去取密钥和iv；如果将密钥key,iv以及appid写死的话，到下一次就不能用了；如果你直接把当次分析的js保存下来，下次再通过该js来解析的话，也会失效的
一个可以将js反混淆的网站
最后一句，空气质量这网站真的不错，将混淆、md5、base64、aes、des加密解密全部应用上了，还有最新加的页面反调试，是一个非常值得学习逆向的网站

四、通过Python还原CryptoJS

如下Python还原已将密钥等参数写死，实际学习中，需看网站实际情况

1、DES_CBC模式参数

key：加密的时候用秘钥，解密的时候需要同样的秘钥才能解出来，必须是8位字节
字符串：加密或解密的参数，字节长度需要是16位的倍数
模式：des 加密常用的有 ECB 和 CBC 模式等
iv：偏移量，这个参数在 ECB 模式下不需要，在 CBC 模式下需要。

2、Python将CryptoJS的DES还原

python调用js三种方式：要么还原js；要么通过execjs脚本执行，要么通过node布服务执行；
python安装Cryptojs的库：pip install pyDes
由于最近很多人关注了这篇文章，把已总结的python还原aes、rsa、des等代码做了个汇总，可点击详见
简单的DES案例可以看这篇文章

import base64
from pyDes import des, CBC, PAD_PKCS5
import jsondef des_cbc_encrypt_text(decrypt_text: str, key: str, iv: str) -> str:"""加密DES_CBC明文:param decrypt_text: 明文:param key: 密钥:param iv: 密钥偏移量:return: 加密后的数据"""des_obj = des(key[:8].encode('utf-8'), CBC, iv.encode('utf-8'), pad=None, padmode=PAD_PKCS5)encrypt_text = des_obj.encrypt(decrypt_text)encrypt_text = str(base64.encodebytes(encrypt_text), encoding='utf-8').replace("\n", "")return encrypt_textdef des_cbc_decrypt_js_text(encrypt_text: str, key: str, iv: str) -> str:"""解密DES_CBC密文:param encrypt_text: 密文:param key: 秘钥:param iv:秘钥便宜量:return:解密后的数据"""decode_encrypt_text = base64.b64decode(encrypt_text)des_obj = des(key[:8].encode('utf-8'), CBC, iv.encode('utf-8'), pad=None, padmode=PAD_PKCS5)decrypt_text = des_obj.decrypt(decode_encrypt_text, padmode=PAD_PKCS5).decode('utf8')return decrypt_text# DES_CBC加密模式
param = {"appId": "cf997823ce9425ec88a91bba9c188ca5", "method": "GETDATA", "timestamp": 1599713841621, "clienttype": "WEB", "object": {"city": "杭州"}, "secret": "1c55273cdbb5c59872f74d9bb90484b7"}
decrypt_str = str(base64.encodebytes(json.dumps(param, separators=(',', ':')).encode("utf-8")), encoding='utf-8')
key_str = "86b4104a75dd865f"
iv_str = "bcefc965"
encrypt_str = des_cbc_encrypt_text(decrypt_str, key_str, iv_str)
print(f"DES_CBC加密: {encrypt_str}")# DES_CBC解密模式
encrypt_str = "Yq5eXIYAamEdveg0mkK/9AU82Mh9C1aqkK0TorSTmY3opnOTd0MjrKiz+CX2VA1fUcef3y6Si1dQWjXphyKeuXr/yTds4F1HH09883BSXRdpiq2lhMx3ZDGj4uryc/g+bBuY2v2/dVyFLwnFI0Ee6/1T5lt5IcPLhZ6lD/2LiS9JemMsmqiRtR1Q+bS6FVW/5yQf9KybgI43VHSVENsovvuI3CUg5oCGLuTNvB1YM1C0f8r30UxrfQW0taugCDv7Pts/7KOytOjnTHu0EBl1IyJdXSGtovUqa/Pw1xZ8SuM0S4kPBQai9ltpUPRcA5+v6hXkY2WNmbnbtgfHx1VxfAXFMkkkVQimFy5J0Sn61Jp4z8/XPM7PxUutVwSJhuHpCdeqQiiNaw2lg95vMscaa9ASPzdZjQlsg6gmy1+Qd53mzJiudt87TCGLUf2KFq+hoer5JSOEl6UH12oOXVBaPPEA95o5lG32oowT0fceE06weZ45AU1vNbgK1/ssdS3PQ8FIwl7/C63F3aY9GNf+YZCkwh0rQJyItlrgPkfV2RyEsNqutw3dfmd4ZBpBjdbB0yL6Nrd3FGWoYPQnS1uzPtwSEcqXrWxjK14yOCb2D6aG4gnEJ9O35fzhc6kzT2H3MjXMdZLT5k8d5kWgRmMo7mRqNVMt2rpB1sF/N7UOEyxP+AzHpHnBzcitcm/dovwIDjVvaQT2uUT/0uqlnqER7cOc91ZFxlMiHRpnDNOU6zxZ2auOorSz1Ms4Jb3zhJ79Ag0z+SBb8YWVJLb8o4kHfGvwjOVnyPFK1k12QlZ61KgZghPq8ese0lpMzmjB1d808bwogViCgcNAokpYzXgj8vGWp0RszWsl04844TDd9XivblEg0dLiUvXeRZmSRndBHMEQiBKosOBuaGF/oIrm8C3LOcffBGlhHchTqnJ8vC9X050uFkbcWWzY1ZpccLkfmOfNb1N5+E6n9lhkNBPhgvMG0VF3Pb3kNKI5xFoWxEzkMlNsyx6ccaW5VNRHu+t9R7fXbCriNqg2VLcB+GNMaf2l6b2FBQiOsD3UxPVsoemCH/ABhqM2XFVdHI+NNo5VXC9wP57b4EHWI9h3YXz3pOnVlEJ7KZD8ZqX+Sb6rrCP4/B9jzU1yxacK74uBl6wm3VanzRFKGqde6YOL1c+6v9xv4Mw47D5aOPBWpRmdZMt0HnmDmV1c+iFbGBHmVhskrGdINZy8tUYt1zDZIqlHljB4lo/ys+NFSdSRrwiTf56A0+QpRoTQJKObLp/NH7rw+tb5+7nPVJR0UCGwq4qRoRH4n2JtWVDlIuK4X9BR5yP8ERpIbCpLoEXPCvaueKR9obcvQH6gEGyZhgr1A88KZeqKK/yD+DHg8dVw+baYRbr/L9F789UOKP29/aj3y5uOzJWHf4l2a8IuiTRvsFgkzM++qa3XAL5PjERaDZ+cWyvECGEn6uPcN2Y67DD/5JT58OtlAAEg0Inp5FUtr8JQPsGdFgVAccuOe7JrTYTeAZovH3f5VHcthZx3P/HqgDhV/nYL0AZRd+sgkwUGYvsTzkRW9w8ZGZrpP+EhZr7xo+A8VCQPecop+78rT0s/rNG42k4RNlVYeJwWo66TL+wI1UnAlaaK9fK6qJzn36wArsM41Ma2bnNROS0SbC52lKfv0Zh3x8fUNAdM4+sfYyhmjMOXyjdDxqiqZBrrnDlzm5ufADHPztT0aX3wscvZHk3UmXIztEpAJbvm1eBkV8hrd7eCkDOoZinLXaooDj9hJn3NrbNqQuryl6/vTkTncvv/hg1pFUjcDI0td2pZ3hUH0L2rcqt9Cs4t/Mg2ZeMPS5/YGZbIjo3aFgu5inYJMcP0OKa1Kn+snuJkSg023hBIqIjkJoE04lJ2hERu5p1FFUuCTynhJ2LkU1Uv9PfHpTVcyaNdBFv4JcfTX68ChPzHe5xaKf87MRTDyrZt++zlU85PY9nallBQBrSS6qcehHO3Eo5ealX6HqRMo55NUowexSc+4AA0EQkHHr14bj6NV85+O80CyDZ/UlYY7syT7osB8nxNt/9jgj8ffi95Z+waP5eFRbfUBao25veFvbbn0xqgJIZzN6oE6dZ97tlOqSAmANhVoIR83pgJKTZlwnwaxXLOETYebDb4dqnymycWLaU020G/x08sOdZbvmt8iMElp9CQ+YYYxjRjw1KzvGEKm21YhSiC3x0zpgG+PEYSp9A7HDUvbCsnL7rQyfjUjZ7iAnNPAe2GYV2OEAWWMa7k0vDch8mf57uNivvvx1UxeNWrmXUNafH2qBsfAIAn5Cw2J+bBuuJbT1W0SDR2mv6/iyZS8FDUm6Uq/0YSXgHfJvNVoZdBgQURwaU9qbCXHMxQWAly2cXTvEkkKoV04I9pVZI+egxercFzB1hL7mhci7ujiJisYV0/PcfXy6/ZKz+UKjacXMHW62xirLzsW7VcTBeyw3ztGZLsgxas/gEmM4MG9CRXp2uiTunH9TwEdJDaVkVB26Du1g6qvi/1yCsvwiqL1VcPMhzF6RXxosaRdErbwn5rp4JFPFKOsGEk1iGCBU1V9nYVJfqLfnB8e7aTti1qA4PFQfU+FjEA5n3usV7cALVIxBGy467GC2lIcoyuD+vT7dFKu8cq/ELvbQVIpKEHaxmn/7sawrzZyVUYn04Cyv+D8cOhSA9jMp8ZuZxvWqL12nTUjpG+m3vjy9jrZgoRA34pMmUOS3ygEaVr5A6WTqqO3h0lZnFhaqebgOpqd9NCS1vgP03QKGaFodb6L3InYxGAwiVqG8KbwE/+b/K9A0KLqEVNKLRCEGjgp5F4hZrDPjTvN/GfX7HMgg4xUr2wuThGhXI/gzLdDKPnLxmeAzuF8cb3CRdygdAjeTwizR094MHR7zxvFQy3N8A86e//GxTXKLsRexQPf6Jki0AFgQixnIIJZ2n5U/9quxYTTH5qhm9+/ZuvWwDPeCSvQWWbfxWp/KjDDq7b1PZgwyed+6Isfm869IwDUY9gVPNZ3G1t+fUSAS/Hg+bw8niL4i4rJTzbsDwA3R8DHy8vhGjpG+mgpUVvJL4tB5UdSSwiPHbWn+EO/tqWxSGjNVdZpob+9P+nSMzlJqqMPGFG8jFihvYNstsQGqYxRNndMzFI/8RnTG170yYuJC+KC2YKl/zrg+ydDK2HpGF7eNL85B5PDbWttYCjNfJJcTLJxzWKs2tvXPLyQRNi9bwroIK19Jdrki2yl6GRHRr1t5cbZXTItU43rmNRy0INs90rqz4mBRe1xCfvbM5GVeIfGkLUrYx1CpzxGl1/CP1opZ5sruYigMMcnWyx7HLIorwwDNkhzy1aD2JYjuyr/cVezkPGPOIomzkD2P2QljJUtfOmBeZTNGWZYxRLOix2bHMERDY8vLRg/GYDwY0cV4LKgx6fco01aH3JB8HXdv6aAUsZVHpz9SP4x4nxfSwA7gqK2NBcd1JtpEtd7jOmwqVxXacvObj3BOgQdfiBYl6NxrHuZ4z+ZAdjtpSxWyCbtalxwIJLIZB+YVuPQHhlVcijBhlDxzcaUpA4fzr0qNy8JYCYLCepAGca8HrpflpMYDqekvkVdNFUj8mYfr1hmOjQxxk4jtS3wc8Y9DaAmDNJ57+3IleNqwpansRbuoGSNl6yQ+IwIawUnL18jEvrSsEH9scAp+S9TwyqlNCMV97IUeAzVYvp8CUGkd3syDwzKMwmFgBb2l9YiMo7+W4BmU1cAYrYocZJYf6R7LtBysY1wZYMuObVbITClsOV8b/wBU5x+72xHIufLc4hX+Rvlb4SChYY1hARLlp3/RxXOKmAjkte95uuq8lRSIzSr4/H3LsPNuy8p5fGTmSSGLr8bkJTZpVJLOyvI7lin4ZHsdl3NiE4iuLCy5xNKRFZuj78RqVpYElMzDsh5PTJhSO9aFnN6kXQQlTA7T9UCV9wKCnSDsXfGP95BksEnpNcPPM+iWL8IMo7ImpsIPd+5v+4vr56tGjijiNYioSJiPyridmISMiCdSEwe4bCMe0mqbHVxL6MFpU2V+KhauOUPg8oYtx3UU4kBFfDomAURNKADELJuArLRGzJjplO7fOb7+9/yLU2boDSeW4d1RiGIJnuxXGFPNE7P+HrWZwIQuR/WFsZJxV6YNEJYwaJAyjSgLH3U0YU5Q6YXI2zH3FuP5RNeh8tiEBsnOhzqJsnMD5dMdKiSwq+PsszkhL6w06D4GP798HquTeiCr6h90WxMc2NQaMHp1EpauP1a1nk7KJv5KbNEee37dJsmqlfhQFIA9Ha9afF99YRiH/VmlDxONm6NHqqwvFR8nI5RgChXcI9FI+/Hg59cCTz4zPmM3Pib+tuMEKiyFaB6U4DlvCuCQg1FKbXOLp7KBE1xEwPG1ck6WNRWDtgFsnwulN3/+TPzq1qmMQo8Vq9thGO1i5/eepV9qklelRnHex+xuK+aCwJzXVmqXmqNzgolUy5Sh1xUBTX3FnXleEz6H8AIc6cPB9uSlPJdMtGNiG4bySOjIX0SAEsjyoio0F2eLMAZiOS6202B4V0LWVyOCW30wYtW0Y18Bw1UhiRI5l7arcOJX31Q5UINkR7QiLKor2fPPXgeJHSHonIhApeo89C1YtokZ4vAQXK7LDw4GEYdZ+Ok1c6KkTsiymz1cFkTF2ssQHR1FOt7NJeMcmm5kjzNinlWEGmmbQD0T9Z0j3bvpio4bhjqcoa31WH7FrHGXNZEkwpg74zBCUwTWt313J9eHQsLe30IxM7WKFN/lox7Q8msJHVenXhi8Kh06dyQ+s2nxR20KCgKK4+GsNZxz6tIejTImWF/7bW7sCh8EGxyqrj9qT7iVQ2Heng8ZQ/lSLFxY8bUW/Yl6tBTk7c2cRLrSWL2HzUU/ULMrP+we3G5MJ2n334pznSAQGdaS8X4hq0+3LEKSHq4atqFqAi6bjQnWtZeN3mY2l05e0c6lIOJdBTk7wLrsWsQ+UxtqMpDP+u8Lawt1xgwArGNKIREEwSxs/L5zuncev3uU7L96+U4ikR4dO4F7NStxvvTHLdt9G6P1QnfWKXDKBJDrtvBi3TiXjbHDbBePsf65HJ8IuwOJfq83JMgcpSkaHuwkEHVEPLZl2L4hWC8zYW8iaCVzopmlCPA6M3BexOBXgGwqvv5QH8hrSGidK96jwFPazLEhKfNoNilwgefuQdx6Xq5hDBRg9kmKt0E/irYbnCkWC8/Ii16cD5oZgZvNayOhXNgUkhczJsrfPER2yFthgPajcCmkkI7WSLXQh0Sgfe8Im8y3/t+Y8K3mm61adjvUpt42wHU4BFPxC3jN/oYAAhVPW3/6Mr5+pc9fGHZBDmMgDt4ZSIHhodif+utt/szr57YHHHxxNUvq+wN8zFPTm2Uv1ObxleXEfjWFFrMm/zr9XZ/llQGbCAnlzBpox2UN5XYsbNVZIsHSy7CEgXp9oPxYBnnjX/MnVgFzU0WoS8Mx/A4Tq83l5nUBnuMpFh6XL3sc7hNZnSSEtxhrkrlNzgcWgKVcORa58WxX58+jbJErqCK2Dna+F2wUtcXGcBcZAUzcGIpSTkiHWz7zOmkIPWpz9bR8ysvcWk3BMsHwsZApcqItjG3Gn8AeFcEKoQbzaYw0jPRBkZvKph1tVbt7b1I1uYup+yF6lPtfUd1ia4sWKXPzQYAAxXpGNQUOnXjXsT5h8RjSGDgKpc1XF0b3IYFdpPgP23UdcZ+MCdfzRTTYJAX2OuSuX7M/0zUffiDrPHsRHUdzCbPbH+4Dm4YVGKx7icWNY412G+wYWcaDlusaOS8G8RLAOxfTnbVYII0tgm5xfZbvdqMj23VgJS/TEaHShojb9ppv8GOMCUtVugC4nGh0BAPzBdlXEZ1aIFRHO0cbBw3E9jXWG8hUWxkYr1t2eHYLmIQA8A3TxsJFqVzvauZCmZYR96bs4yL9G49AHwCY4WImdTn9dJD3VvCH2qH6T5XZHSw12qQioIzL+eB18APC08ysykoSGzBmpRy7i37Cb6htI4E8v7lTT11hCu1dtVt71Ev6kHijWBmHSkv4lr2oCA38vV9oyJ8LfiDWJF/lW9bquv7VUbGoj5dWpkE0OLPnNqG1Foo9fwFdZ052HXTg1JwlG1+6w8cBIUqBjXJRcOlw8aqKxyr++pPq2o9h7qgFDSgKtHe4cKZTYbzzSXEKtSf8Lt72sEbbH3wKYhFq7wmhSbgMT11qivt4LGrANKSuWXSnqfZB3Y+0yabQ6LZucG2jwr+OQI9Fv3h60J0X6AUskb5+nZGESJG2M7Gh6Y2QO9q4oK6ZhX1HS43pr9Q56GUQjOWX5lkGmisF2OcslpwL/AF7XCAsWeBMZ0B8y29mNsuepZKVlnk9MXxs+R2zp5TbcqXbTzx7l/S7vfPGZ4Fta/PPdnqDULogy+apKEfogtzEhyUCbTMcAIBpfQ/y2lkDuddUO+KUyNfp4TGwIMJgt94zwb5rQI0FelGIP9mrct1Vryj5w0CTElDR+4fafGurrMYVRZrly2KNQYuGyZtGnuecBtQum9NAjfWNU7kbjuwVm0iCokgnFh6Glx5y6uhbDHhQok3Tql8S1vUCnpz/TBTsdzJ2zfjf/Em9JssS54Mbu01T4tQVq8k77xxdK8PJ8pC1LcdVBI6e90wBALGRWdjau7T4GzEn0gGqrbxqBt2Uo7JfRhwUxht2XU1ikbzy5o+lVyXKmg56C2I7iUZZuM9dnpf5fIIyyTfMiaJSXtN2gl3ML6p1bB7lYLt1WpCu/KsKTokD29V+l/jIjg/X3ronV4yNmp3lNfB7xbbvSw9d+VJXXapIoPNKOeTspFTVW1xq5lvSmz0zPHTDhtF6AUvkz6o1VYp+6lFStj4jyzgWofsjnHfSmbEHN3Lp9wFthw4dFSM4DGvoAAHB3KaHJirY3ms1wjo8Ibtm6LgnxepsXZTJqdXtTmY1uBHKZrLv/R7Wr6ASBAYug9Hyk2WY6bv486JSZmtxYKFDQjD2Xx0tLtT96Lm90QD5V5m9IgXy1vngdERxsXctbxUDk6jmjSAq8A6qwwWXc5F4y2c+KybuslCVRPH6LIcSyrqSw+hIkDgdB5gN1e8/lkBjZzsY3bSiBYJwpGV14MNEkSf86gyz1EqXRax+nj6WiojZw7Gk9wdnSCtRKJ69VBh22czbbf7rU/QciTstDWTQ4WLzC4FeuZB/1uyw91547XEVUNg+bT37rabnvt29SjhpqKJGB4QgNfbQ+N9JnOlcofsVyWdweo72vr42U8BZ5v62yKtwk+qKFYCu1IOdiU263o8MR5O5vTE2wCMhkYwWkGXQOF5mCCEBLGdBDqJG1+YWzqCC7/S/+MOTpsNYdTdnRk7Llj95D5XCgXuzixgSHW0Ynj9gIP58jniU1QPqEId/0xtxXUVWdNh1PlrMF+S1NdJU8GmIZ/grO8ZLD7E0zpLH+h/GU1f2ykudxesmGl/LZbx0IAhVT0fGjH5pvqFmJB2hv/sjQjROcEsHb6+H5xEV5XOQ5m8MrNu3qWtL1Ep/+cW1PLzTYpRBr2tFATrp8dlhW5ByaGaBQbq5McFvC40jYjHrgQ8DB7un3peGzTSZZmemF3sbSmENKRZdnmTyFQpKuA4imHkkF1bXD2WGqdVjE6sLIx6DeMObn7hY3lxFoT01LzO9RFOInBvq83DrtYDvNvYhTNpaw7FlflLQPNpZ7FR0LSaNgwFc2qs4zqkRveqz/juztgI4NntGuOtpANIne8IK+O2ulYnqjV1l4N9ktvNyDNEB1SQYK1w1W6PjH1FOW42kyXVTK6jtDrNZPAz0zrOGJ4fohsWuOhsF0wa49btJbKrjRxvMrt1nq9tgvjXwHVpt5AI9kA2WNoRUHb8i1dE3dFrdTe8yfwH7d51DPfkhIesrgPfkfSyv2cgMdfhm1CzdNkk84/p0LDfg6CUzIw1u1gjHnCtrSMcRkbxc2KaysVYySzX28lEaICAoFdTBB951GLKIi1u5hQu+7zptVofNib7UpgLYDp+Oo15pR+RlvXZRKLUFGk/0FBplprOVAkbx1PX9uxBWVx/zBtX3rYnO+wWQnVDrWqt4FxPHE/Qmc6HDPCI/uMkzqDSzMIhwrusp+4zA/SHhhA9LF03tJwDZs/XmDk7bsK1uM1LRT5fNHE1tFljSJuVDydOUT6ZFXtbeI6dWjsi5rc5wQTgfC/wwGl0tDnZs8wHgCstYSlN82NCFUa7Y1obyi3+UBpydSlavC0qTLTTNV5WJ53fDNhPD7z09ZLorGTegdQWKSHg70G7aEzW68bhRyQMl4IAG3llZ2Gakl0jvAysTRCJ5Ulemie2UkuOBgfD1afCmt0Q4fOz7NWUBgJCu9+Rgqbmu3XUX568w7wRttbkBDcTsA4xCmilYZU3SgPxMLCC4/5qKqXvDyTTDeSWYybmviDWuYbjxZp9vDUg3aSn/qoI+fRXPlIxjb+FDMT7nafnwHELO9xJAaifmpDNo054f14leijPiOVon/XDVc3FkEh8udf4j3GcgXM3u0dTLF9DupD7wDPch1OfyUj/GpJbJugB6tsLoFtx7O+tFFSlVsfTNnYTJaf829PuQtHpiHAcAz7EvjSBI+qob0lfw9qUbqycWf2VdRyD3E231neVsgQg3Euin5n113oi5JUoL3ZmtFt/eStcgBxq4c6/JIDpLwPySsDGidWZ1w4J+y6ar6nvH9PYi3LQ6PQV8893ZHGQxD8B10dWOUzBZahYjiha7kmNsMJ6K/likQ2yXdGgiOx8AOXkBQfbomqXusG1i6nlsY55DbZ/OK0d2VubyCze3Fh3LbafzNciA/dXpFYGFTgWtkkXjChspraWshL5QqJCl1X0CV8rHcmH8Zwi/hbxt4WyMrt9EOX0XPV5zGZbt8qoMlkN/ei2E0Vd7jd6yCVTNpZZJGmgzszZt2lLSZvG2MJ6cnYdzWTg4yddnTze5xkao+DahesZD7PNkVCue9W1wgAzSAKX4rKay6iqi1Mqq9V2U9xqhbQS1R7bM/0vErXDtTdo6YCdNqgYyToDQLS9b0eLk8/igFJGHRW+A947xJV/6hjFOcjNv+YK+NM7cYBYVV0+Anp07Inv9eV9sWSGR7hWlCrsWJ6pnw75gSRdSJPM0lhpYwxNIVZOhMG7xS8f4hxyBAz+QTXiEV83Hrr3KJp79eik26YB/tBtdng4Dykl9s3jTpZ69XCV7wZ75Na0jHTTLzc96omiGiCGAOD0oZ4iO8R4yfJzwC8a01b/hu9NcROfwMeDwd9IMYQIun3MDyoXdSXk2wHlEbak8q5VAf0Q77KBHGOGIclvIbfLHJi4XqHnx+shzHhRms/cMbfZz3sJN5vieMZiIiU8tXyFZWwn8ntT3E3KWmVKSowyuUs8idqUzXcU5tZ0YPgdIpgmdsECYavWP5Lus7uppzfo/a1Vzt1mskZH5Rhy4bEZpfa/1O4AQJ59NDKD/+XCCzg5GfFIhx/gmWzUrAQA7W1z4gnDlvxikQ3wB5q1X3qPAwR20p89xeL/EwMXhR5cdZXcT2aA1Quyq3V1+AAz/ATLK5FLXhP6BPcx6pkjIk66evz1cX9StlRwpbMOLCw2xy36a0YJ7ugS20fLdnc8b5Obp37nmgzj+MSTV9+45eLf19Qb+vwvPUn8aN+LMjrpa6qpdg5NNh+ZvBLTpXIpueLo2bQ7cqAq6lhd+snQ8/95Cp808WIM6xzWHBUzc+4bAEx5WIuf8DL51MS4RjsspwOw9pgFLmjhDw1idaUuvaVx4e6VssorCP6dcx6xEWqQRbOz2kTdtAyTP2SaRb6VFTtfDucjASWq5Z5z4mgsSG1EVHYjpUmsNfPt4cTnRutRFFsbYJ0f85bFZLT1zIBa4ViyCzXlablVocM56nBdcGKtWwUcKWqS0/NJbwurlFHNXECgLoB6cNc3P2WbYyjx/+JHTLocAidx09IhMjHUysIpY6tJZd/w6jYKzIl5OiMXcCp5jN875Dty1YnG+P761sBa/CtsZ0NCzoxWgybBuelPb7KFH3VkDePStS6cEOD4gW4ZizH3t/HyImnX4pTr36iJQ3RbyyU6lTjwXHFirM46km/A7lf8DB3p3rcoCigOiLwm4GyzGpWmNOgMqsRW6tsXADujDvQCxdp1tzuLsGk9gsYIiUjXUPJniCRm7Ve/Q5jllqg54W873jnlI6I04PuJ6aQ0I42LUe/YYk/H5Pl12oOTJGf6qR8S4Y/e5elZO7sDQiYB/B8aMvwRou3cCv58CfFDDjEKzJNr5/LJI7l3rGzCy4f9pNGuxbar2uX9vhcSeflcNhfyEiaWIHwbpJ7CEQkf9vaXhvwdkbQcLZS71zMcCGpGICUH6/u6ZGl2XBDtY6XO1p3lUBZyApvlX4205j4ys7zifAkLDa3hjsR+Ija4ZwYC0gmSAJHUgiuZaY3YN/TaNJUYuYOSAh+/fF4BTbOiZh92vrd3k//lXVZOZioi/llN7UuEtNPMthzM2ri8ZfUClbwjGca/LMWt2Dmc3eI9XIoU+yneMxZY6tqTQhXBAR6eJ96l+g0wYT+q2tS0xq8NllVbUn/K6yq8nkjdFQNoub/vUguvEVKSdAQzS2aauv4+vsVOkJA801zNKbk4ClcgkWXBNTq7NFrWjImKTPGf9xG4gY/2pGXZ+00g26REj8cRUng7e3ygOurCCyPwd1cR0wHWqq+xNUbdCYUIdSNZKTH/Ztxfw0ouHY7qR/gwEqdGQ6262b0VduxJjDjr5DiJmHUDo0vFNRg2o14xGZ+xxBox+ET/p3J3f8bkOdwbifIIasVMDpxMNxtZLbre7f5GG1FtRedZ0b9sS38Il2s/7eCa9sq5v6bJEquYmnE9ND6jL1A/2Mi4XiW+X6k2dpj6rhKzH+yGo0ZHLiwRLV3Cgsz2dTtLyP46qPQ1B7N/nv+zRjMyOyFmNldyE2l6hpZQ8OEZbuy94i8YOh/xJkLUDaWFEUL3E7bbq0VN0wdEUKOOY4gO6sF69jiNaZ6HAqpu8Gh2jOISU/PKSIAjIF7heKPIaW1Z8Gh049AfMDtPJnAsuuUOhEgnzOBeCdK6oB23HIn4J9JdthXyq7NxaKUmqir7x85CaBnMUQ3r6D7S/NcjIUc6AOsYcy0osiVRgwa45Lt9k8XfkffYDQ3qulbohD0Fsu/miqQbFcZYXn/1Z2Evu9cPVhnwp4uF8dsslTszZGhaXQfu1R/KmR/qfPi6Fxh1r3r04JRCObGFCef3w77y5Mdt4azWzJRCsEBQutKB1uUg26r+byrTFmpQyMhk8bZK0M/aLwNmftRYr2z0hsRiJXJmJWJs7Lsb84vyHIVVBefiVn8Uj33s8v/wExCof3sOgR0SO3S5kbNHo1hCQ7u3Kl5cW28LR1xXyi/FGQ3a0TqR3Qb/nda3Ktx+vaB6zlqNkrRAZ5Zd23tmgyhneBcgMw+Eic3NVvwI0i0pWX9+Vne8zYiuOx4CgebzLfBsfu3RklEk926S//SAkWiPVS3394R9/RGFz0SjV0FxJtu6UEGlvPXGdltE6ZXCN0y+blZTWnJTm1OKEvDDASyWAOyhEBYdfBpvJNqZql0649bwyJ+fk/Bb9vLcQMoBso6eSmqFDd3M8u0hqOKAxQLumhsIdmzQq57N3qFEYVeEJdviUK8FLEnr+2q81MIhpUSxxLLG7GJ3JdxpZA4qlN+GNu2/XLV5jINesbmkbygr7SkeRLen5shK8kwgI6+UkoHHZ/4wiLlydzhU7T0NPwrr77pm7VxAMqXwa1Sn8F0Xjr9N7ConqXttQKQkDGnpHd9juNBQe16Dlqiy3gE30wljYsFryO1PEYwN/kDCkd5WJsQWlnHOcwdBzdt1K6iuwXI676ErxwmISEBUeKFI4N75VX2gulBie/CFSJ8bs1zjwAiQr+5dgnKUbXzU8bmhMKyZNRi1kGuF/c+ycWiH8+ZzSti8viN1RY7gjljaPn9lsyh5+vuFhDeuEUK4ouGqXMRHmji2IIWIoXzqm9gE16JMHxg0Yrdpr1OCSGWDg2Fx6+suYqMeTN3pjzZUTtMI1F50F7NzmzKUcDp3SK9Dy5JWx+Vyr0n9o4R52p+Oi/temx1phcuCcJAl+Xe0tciRN7y2crRLLrzXRWt+K9kJlEXjx4apkiQyyHvNILJAeSxNH4aFf3OpSy5tfZpK7pENDETpQoIHnzw4XuXPH9/T0DSYi7CusbddoRBb/7Hx9YjflwrzI46EPyZnJxihxTrPdlJvNUgHs06/CnqiaKGkmqPYx/QzTV7x8DIPX1tCEkzfTpIevnRDeFek19NG4DHKLvjvs8JcSFxlo58mq//prqe+njHrJ5jccSe6vXciRRRHzIk9GuVVQTAZ4ISEsJOH679311fU2tAvqTI1aKCCm+JZleuMvti8QpHDry/Z/LSNL7asuQolESTEOCjy8JylOi+DTAcrdtbT7hTJqHy1gGTfAcWuSDmdQv3N453pp3itrozvvw4dhA/cWDRWnRZjexaHgMe5c8yDptdhdVou46ImcQZStiv6Zh747kEKV/0aLJMnyYXswrmPH1L3yPfWTNDR6YpgMATuPcsOZn5fhEyf8ro1dar3UUDDk0RU/M3CKG7V+hS7YnofWWLBQUnqDdTIaoeQ8UkPVLVyoQInTk5l7U7FiZJok161Y1Z1Zk9jfPaI2qipvOGcMYwEqnvvfDPBuLdnstxdwuf+ViWQHE5pr0wNROp1iLgLY8RdghCXnTsJf44raxoCKhftgDRt1pDOFAVmAmW2T51A4hsOSYg6nHmcsTRjvYmT7v3DMpEVz0MxCpyn+rsv9Z8VsT6Pn5aJXlEkPrukWYulwRIH7FIwq2oGIs0tEL/DxNvYWuwawWZT/Dvila1KvfMWLWGCzQz/wcDTX0x034aD29OrfgA7/k4WsfK2DeZ25HIaewR7T7a6YwcvKeM76v2UDBYYnMAcle0gTQ7Ey53moR/TIVsPoJ0tXzEaGLAthAyDoN64MNquUqwbZYi8c9WG4kM771/vgyYEAtuHL2+A4XGKY3zBzA0vykVJmiovHk+ybLYhy6F1Pn5FoOC9UvBmG1y0nAtzLBkPw9pZUNv/JRdy1qQ8hJPhen7A7ZEEaRgcSEkyF00wcjmNIev5bU1543l+RTRJQJrANR41Tq3XF7dWmWhIbnk2D9qTlpEsq2hg/++V6Z9/BFHMU+rnbj20eZJ4n7O0dfA1i0BtAnHY9xIHk9R7JRUlWy7NfLO/JYCASl6U8qV/rYR4HE0RaJcDWEO90i4ubCYxjZjqar+qh4FgROK2l6F0C0uObVAWRGrwsBXnQfWvRg9xkkl9jAlcytcp9LpdzFu8TRdGsQoS9skSY8YjoDK70oBfzMW3ceNZnfEUMLIgL1NMBSveAIprHY3dkfIYDAm4EXnypCAA+65TJ9oAHFmiJxp1iIAagtDWtFWPQz1wtDHrRZm0Uz76sWCVQC2Cm2f3C+aOpdG7tUn8HbEVIo9uYrbd4zowntw5ldJXltndBf8qHbKXW0BeqrAZ5P5UaqH1ZN6XpmpELQBG97MRvVfHxGblJq7l6x4XVhQOZ303dO858Y8C0w4nbPUPDtvsjLrwvrrfweGV8trfLoT7h2Pp+x5kGV9npSKBv0M7OBBClMOojrFEctBtJ8VpPaFHJ72s+duLJM1Rn+tX4B3o3LCijBBr8vCnipO8/HTvVSBMdUgN4AH6a0DJBjfdiuNdYipr+5FktHIp8vgzXKaRuM/r2aKVu3ivrQXgejTBVJIXnMYy4Ci/oqz7ydlF9QiohyvHPTRQryiotbicRp6qKPxq7xjMJEn901YzgDmgQVsUoM+sWWaJ4LjlQo1odGWiNPvXDBvBttLZ+DTxLxwiE4lmbY554y3P3cDsLgbTDU9T3t79w5ER9la01Zq0cz3O/zTwFdnVmT/ci5nmPQws79I77Smg825F+mKre1jHVXQ1XrFACKnLlsklwwppViosTB3xvX8AbOsfNnbLcFtcTqIs64eVlc/HZC0vla3JdpgpufZBA8P3YoWSIc+TLb6qKpd3y/dJSrvL7NOiZG+ME7XOzSUly+VUsyBkN3sWjR/+tgtKqrqxNcSbIXPfpt79F2df4FKvwjXq4y4QvkjYWARc1Q3R4nMflw2iXbzBhLHJrwBdkynRHzT3oGuM/0KUp5XwuvPArZ8DcMHp6jJAvmZSM4VKHMcyXFD5XwZoZM0brbugeWABwlCmJaTNDSdF26Rpcuz2U5cO6+ztfmIerHLDn9Vd+6k/+AZRTXb2uQN0JBs8jPKnqZpiMXV5wgJu87nzm2oG0hWo64sW+u0UA50aMfgELTAJVkWff+vlqsovFlwPtpT8D3+B0f2TCvloichGO9iWWxQvqp3OuBdVOi+q8mqQJpaZc1iMzU0hjFBmZmqCjRU3dkBX9AFvht620bbRhz5t4NknGf/vFyiwx3bUfiHphfxHUZo+rcqpRCZpk4Wc1+8wJswVWajaSNabn/fuCFli2JaKsMQmbyUCbhsUDkkCxmk5rmbLi4vcJTJLJp8Awos0ySZZffx/ZOmyPEGr3q9A0x+2iyawQe3jDy80Njpu+H2hwffMXIIORUgpTHxBuIyKnY26mkA3iKG68h2m2NSp8zGSLhcsAq5vFXVOr+icpMw/chZz1COxUpcw76d2px3AcwG5TKRJg/ljAFmW9x/R+ac+ZrYHX7xlrcqi76knTJ6qN6c2DBGm12HVibIpE9F6sUHYRpvCPKbXiBmPv4L9VrvwqJDxh9IdFj2aJXzbzRaf61Xllty1ZvaAq+Fmp3lrE6ylNsXwyw+qnlmYs1n0/NixrFervhDfsm1zRxU3f5Oq0hNlQKVZPzz/jbaKW5Y7miXREXmXKH/AmcWVuNLEdV5RGrXNpM0hlAeFTTNwvAvc/YHIs9qkYnCG7ZK28Sn6pOdZvaY8Lw8Iu1BeEqlzGWn5rAlku0pzhHAORcbGy1Zoukyc8vEloAUcXkcFTKTb7PkYE5SLZYKm3nxh0/glzLJGKWSMW4Z+MJQ"
key_str = "5fd7461e72c4b59d"
iv_str = "6168508f"
decrypt_str = des_cbc_decrypt_js_text(encrypt_str, key_str, iv_str)
print(f"DES_CBC解密: {decrypt_str}")# base64解密
print(base64.b64decode(decrypt_str).decode("utf-8"))