1.UNIX口令破解机。 使用python自带的标准库crypt,我们需要使用UNIX计算口令hash的crypt()算法。当我们要计算一个UNIX口令的hash,只需要调用crypt.crypt()，并将口令和salt作为参数传递给它。该函数会以字符串形式返回口令的hash。如图：

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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image>
完整代码加群：683380553 获取！

我们将口令”toor”与salt“HX”传递给函数。该函数返回口令的hash——字符串“HXYVTCRlS8dMQ”。成功！下面开始编写代码！

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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image>

我们创建了两个函数：main和testpass。main函数打开加密的口令文件“password.txt”,并逐行读取口令文件中的内容。每一行中的用户名和口令hash分开。对每个口令hash,mian函数都调用testPass()函数，尝试用字典破解它。testPass()函数会以参数的形式获得加密的口令hash，并在找到的密码或者搜索字典失败。该函数首先将加密的口令hash的前两个字符视为salt，并提取出来。然后打开字典中的每一个单词并计算hash，如果计算结果和口令hash匹配，就打印找到密码，并返回它。否则返回没找到。源代码私信小编001 获取！

下图是我们创建的password.txt和dictionary.txt文件示例：

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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image> <tt-image data-tteditor-tag=”tteditorTag” contenteditable=”false” class=”syl1553321092536″ data-render-status=”finished” data-syl-blot=”image” style=”box-sizing: border-box; cursor: text; color: rgb(34, 34, 34); font-family: “PingFang SC”, “Hiragino Sans GB”, “Microsoft YaHei”, “WenQuanYi Micro Hei”, “Helvetica Neue”, Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: pre-wrap; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-style: initial; text-decoration-color: initial; display: block;”>
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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image>

测试结果：

<tt-image data-tteditor-tag=”tteditorTag” contenteditable=”false” class=”syl1553321092538″ data-render-status=”finished” data-syl-blot=”image” style=”box-sizing: border-box; cursor: text; color: rgb(34, 34, 34); font-family: “PingFang SC”, “Hiragino Sans GB”, “Microsoft YaHei”, “WenQuanYi Micro Hei”, “Helvetica Neue”, Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: pre-wrap; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-style: initial; text-decoration-color: initial; display: block;”>
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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image>

2.zip文件口令破解机。

需要用到库zipfile中的extractall()方法。我创建了一个1.zip文件密码设为123456用来测试代码，当然我们还得创建一个包含正确密码的字典文件！

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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image>

我们把使用线程操作提高代码性能。这样可以同时测试多个口令，对字典中的每个单词，我们都会生成一个新线程去测试它。我们还加入能让用户指定Zip文件的文件名和字典文件名。为了实现这个功能我们使用了optparse库。下面是测试结果：

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<input class=”pgc-img-caption-ipt” placeholder=”图片描述(最多50字)” value=”” style=”box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1);”></tt-image>