声明(lianxi a15018601872) 本文章中所有内容仅供学习交流使用，不用于其他任何目的，抓包内容、敏感网址、数据接口等均已做脱敏处理，严禁用于商业用途和非法用途，否则由此产生的一切后果均与作者无关！
前言(lianxi a15018601872)
挺有意思的拼多多滑块，搞了一会。说一下踩坑流程，和破解思路。
1.anti_content
这个以后再说，搞海外拼多多我会细说现在就算了。以练习滑块为主。不过这个anti_content很奇怪。我仔细看了一下他的检测点和本地做对比我之前只补了60多行的那个竟然能跑明明挺多监测点都被检测了。不过我今天随便把他补完了本来是本来说不想补的后面说原因。
2.滑块处理
主要需要这几个参数。
verify_code是把识别的距离做一些小处理。verify_auth_token是第一步获取的一个token。
最重要的是anti_content和collect。collect里面包含了很多信息。如图
'verify_code': verify_code,
'captcha_collect': captcha_collect,
'verify_auth_token': verifyAuthToken,
'anti_content': requests.get('http://localhost:3000/login').text
imageSize固定的。del是轨迹开头坐标，mel滑动过程 ，uel是最后一个滑动轨迹坐标，最后一个是图片加载后在页面上的滑动轨迹和mel加在一起。所以用贝塞尔曲线直接构造就行了。注意时间戳就行了。我刚开始以为是anti_content没补好，在滑块处严格检测所以补完了。
[ { "KEY": "imageSize", "data": { "width": 272, "height": 198 } }, { "KEY": "del", "MAX_LENGTH": 50, "data": [ [ 32, 180, 1719757842387 ]
],
"preTimeStamp": 1500000.6000000015
},
{
"KEY": "mel",
"MAX_LENGTH": 400,
"data": [ [ 32, 180, 1719757842469 ],
[ 36, 180, 1719757842485 ],
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[ 223, 165, 1719757844300 ]
],
"preTimeStamp": 1501912.299999997
},
{
"KEY": "uel",
"MAX_LENGTH": 50,
"data": [ [ 223, 165, 1719757844435 ]
],
"preTimeStamp": 1502048.299999997
},
{
"KEY": "mell",
"MAX_LENGTH": 200,
"data": [ [ [ 200, 44, 1719757841775 ],
[ 200, 45, 1719757841791 ],
[ 192, 61, 1719757841883 ],
[ 188, 67, 1719757841888 ],
[ 183, 77, 1719757841903 ],
[ 172, 94, 1719757841918 ],
[ 159, 113, 1719757841935 ],
[ 142, 135, 1719757841951 ],
[ 129, 150, 1719757841967 ],
[ 118, 160, 1719757841983 ],
[ 110, 166, 1719757842000 ],
[ 105, 169, 1719757842014 ],
[ 102, 171, 1719757842031 ],
[ 96, 173, 1719757842046 ],
[ 89, 175, 1719757842087 ],
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[ 44, 180, 1719757842239 ],
[ 39, 180, 1719757842255 ],
[ 35, 180, 1719757842271 ],
[ 32, 180, 1719757842286 ]
],
[ [ 32, 180, 1719757842471 ],
[ 36, 180, 1719757842487 ],
[ 44, 178, 1719757842503 ],
[ 53, 177, 1719757842519 ],
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[ 223, 165, 1719757844302 ]
]
],
"preTimeStamp": 1501912.299999997
}
]
结果
编辑
总结
1.出于安全考虑,本章未提供完整流程,调试环节省略较多,只提供大致思路,具体细节要你自己还原,相信你也能调试出来。 lianxi 看首页
