Safety - Misc Mingchu love (Xiangyun cup 2021)

1, Title


2, WriteUp

[1]. Get compressed package

Directly modify the suffix of the document to zip or use binwalk -e [file name] in kalinux to obtain a compressed package

The contents are as follows

[2]. Tips

In_ There is a key in rels Txt, said to move forward bravely, indicating that the flag is likely to be in this directory


  • When you first use 360 compression, open love Zip, the file is too large to respond
  • The content opened with Bandizip is as follows. It can be found that it contains 129488 pictures,
    It can be found from the file size and CRC that there are only two types of pictures

  • Binary is represented by the size of the picture, the smaller picture 262 represents binary 0, and the larger picture 435 represents binary 1
  • Because a decimal system can be represented by an eight bit binary number, the binary of the first eight pictures is 01100100
    If you convert it to decimal, it is 100. If 100 is ascii, it is d
  • Similarly, the binary of the second group of 8 bits is 0110 0001, representing the character a
    In this way, the idea may be correct


[3]. Extract compressed package

Use python to create a script to extract the file name and file size in the compressed package. The reference article is python creates and reads compressed packages (zip type)

The general idea of the algorithm is as follows:

  • First extract all file names and file sizes in the compressed package
  • Because zipobj The file names in the namelist () file list are not sorted incrementally, so you need to extract the file name and file size into another list first
  • The first data is empty and needs to be deleted
  • There are many small lists in this large list. Each sub list represents a file. The first element of the sub list is the file name and the second element is the file size
  • Because the file name is in the format of out / name, you need to delete the suffix and out / string, keep the number, and then convert it to the number type
  • Use the sorted() function to increment the large list. If the elements of the list are all lists, it will be sorted incrementally by the first element of each sub list by default
  • The subsequent operation is to replace the file size with 0 and 1, convert every 8 bits of binary to decimal, and then convert from decimal to ASCII characters
  • Finally, write the character results to the file
# Little Author: Little Fox FM
import zipfile
lis = []
result = ""
data = ""
size = 1
with zipfile.ZipFile('love.zip', 'r') as zipobj: #Read compressed package
    for file_name in zipobj.namelist(): #Traversal name
        info = zipobj.getinfo(file_name)
        file_name = file_name.encode('cp437').decode('gbk')
        lis.append([file_name,info.file_size])
# print(lis)
del lis[0]
for i in range(len(lis)): #Processing file names and data
    lis[i][0] = lis[i][0].replace("out/","")
    lis[i][0] = lis[i][0].replace(".png", "")
    lis[i][0] = int(lis[i][0])
    if lis[i][1]==262:
        lis[i][1]='0'
    else:
        lis[i][1]='1'
# print(lis)
lis = sorted(lis)
# print(lis)
for i in range(len(lis)):
    data += lis[i][1] #data size
    if len(data)%8==0: #When all octets are binary
        result+=chr(int(data,2))
        data=""
with open("2.txt","w") as fp:
    fp.write(result)

After execution, a section of characters will be output to the file
This format is the base64 form of the picture

data:image/png;base64,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

[4]. Picture conversion

Find an online website and convert base64 into the corresponding picture. Below the picture is the flag
base64 image online conversion tool - Webmaster tool

Keywords: Python security CTF MISC

Added by Thivya on Mon, 20 Dec 2021 03:34:43 +0200