2024-10-27 Revenge of Discount Programming Devices

First I tried strace -o output.txt ./challenge

And dumping the data from that file.

import re
import binascii
import codecs

def decode_hex_and_binary(input_file, output_file):
    """
    Decode strace output containing hex and binary data into readable format
    with improved handling of binary data
    """
    def hex_dump(data):
        """
        Convert binary data to a readable hex dump format with ASCII representation
        """
        try:
            # Remove quotes if present
            data = data.strip('"')
            # Convert escaped sequences
            try:
                data = codecs.escape_decode(bytes(data, "utf-8"))[0].decode('latin1')
            except:
                pass
            
            # Convert to hex representation
            hex_str = ' '.join(f'{ord(c):02x}' for c in data[:16])  # Show first 16 bytes only
            
            # Create ASCII representation (show . for non-printable characters)
            ascii_str = ''.join(c if 32 <= ord(c) <= 126 else '.' for c in data[:16])
            
            if len(data) > 16:
                return f"[{hex_str}] ({ascii_str})... +{len(data)-16} bytes"
            return f"[{hex_str}] ({ascii_str})"
        except:
            return data

    def process_line(line):
        # Handle write/read calls with binary data
        write_pattern = r'write\((\d+), "(.*?)"(?:\.{3})?, (\d+)\) = (\d+)'
        read_pattern = r'read\((\d+), "(.*?)"(?:\.{3})?, (\d+)\) = (\d+)'
        
        # Process write calls
        if 'write(' in line:
            match = re.match(write_pattern, line)
            if match:
                fd, data, size, result = match.groups()
                decoded = hex_dump(data)
                return f'write(fd={fd}, data={decoded}, size={size}) = {result}'
        
        # Process read calls
        if 'read(' in line:
            match = re.match(read_pattern, line)
            if match:
                fd, data, size, result = match.groups()
                decoded = hex_dump(data)
                return f'read(fd={fd}, data={decoded}, size={size}) = {result}'
        
        # Decode hex addresses
        line = re.sub(r'0x([0-9a-fA-F]+)', lambda m: f'0x{m.group(1)} (Addr)', line)
        
        # Handle other string patterns
        def decode_string(match):
            try:
                s = match.group(1)
                if all(32 <= ord(c) <= 126 for c in s):
                    return f'"{s}"'
                return f'{hex_dump(s)}'
            except:
                return match.group(0)
        
        line = re.sub(r'"([^"]*)"', decode_string, line)
        return line

    with open(input_file, 'r') as f_in, open(output_file, 'w') as f_out:
        f_out.write("# Decoded strace output\n")
        f_out.write("# Format for binary data: [hex bytes] (ASCII representation)... +remaining bytes\n")
        f_out.write("# '.' in ASCII representation indicates non-printable character\n\n")
        
        for line in f_in:
            # Process the line
            decoded_line = process_line(line.strip())
            
            # Add explanatory comments for common syscalls
            if 'execve(' in line:
                decoded_line += ' # Execute program'
            elif 'brk(' in line:
                decoded_line += ' # Change data segment size'
            elif 'mmap(' in line:
                decoded_line += ' # Map memory'
            elif 'openat(' in line:
                decoded_line += ' # Open file'
            elif 'read(' in line:
                decoded_line += ' # Read from file descriptor'
            elif 'write(' in line:
                decoded_line += ' # Write to file descriptor'
            elif 'fstat(' in line:
                decoded_line += ' # Get file status'
            elif 'close(' in line:
                decoded_line += ' # Close file descriptor'
            
            f_out.write(decoded_line + '\n')

if __name__ == "__main__":
    decode_hex_and_binary('strace_input.txt', 'decoded_strace.txt')

You can see the files from strace

Extracted elf files from strace output

import re
from pathlib import Path
import binascii

def extract_elf_files(strace_file, output_dir='extracted_files'):
    """Extract ELF files from strace output by following write operations."""
    
    # Ensure the output directory exists
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    
    # Dictionary to keep track of file descriptors and their data
    fd_to_file = {}
    fd_data = {}
    extracted_files = []
    
    # Regex patterns
    # Updated to capture both 'write(fd=3, data=[...])' and 'write(3, data=[...])'
    open_pattern = re.compile(r'openat\(AT_FDCWD, "([^"]+)".*\) = (\d+)')
    write_pattern_bracket_fd = re.compile(r'write\(fd=(\d+), data=\[([0-9a-fA-F ]+)\]')
    write_pattern_bracket = re.compile(r'write\((\d+), data=\[([0-9a-fA-F ]+)\]')
    write_pattern_escaped_fd = re.compile(r'write\(fd=(\d+), "\\x([0-9a-fA-F]{2}(?:\\x[0-9a-fA-F]{2})*)", (\d+)\)')
    write_pattern_escaped = re.compile(r'write\((\d+), "\\x([0-9a-fA-F]{2}(?:\\x[0-9a-fA-F]{2})*)", (\d+)\)')
    close_pattern = re.compile(r'close\((\d+)\)')
    
    with open(strace_file, 'r') as f:
        for line in f:
            # Detect file open operations
            open_match = open_pattern.search(line)
            if open_match:
                filename, fd = open_match.groups()
                fd_to_file[fd] = filename
                fd_data[fd] = bytearray()
                print(f"Opened file: {filename} (fd={fd})")
                continue
            
            # Detect write operations with data in brackets and 'fd='
            write_bracket_fd_match = write_pattern_bracket_fd.search(line)
            if write_bracket_fd_match:
                fd, hex_str = write_bracket_fd_match.groups()
                try:
                    binary_data = binascii.unhexlify(hex_str.replace(' ', ''))
                    if fd in fd_to_file:
                        fd_data[fd].extend(binary_data)
                        print(f"Added {len(binary_data)} bytes to {fd_to_file[fd]} (fd={fd})")
                except binascii.Error as e:
                    print(f"Error decoding hex data in line: {line.strip()} | Error: {e}")
                continue
            
            # Detect write operations with data in brackets without 'fd='
            write_bracket_match = write_pattern_bracket.search(line)
            if write_bracket_match:
                fd, hex_str = write_bracket_match.groups()
                try:
                    binary_data = binascii.unhexlify(hex_str.replace(' ', ''))
                    if fd in fd_to_file:
                        fd_data[fd].extend(binary_data)
                        print(f"Added {len(binary_data)} bytes to {fd_to_file[fd]} (fd={fd})")
                except binascii.Error as e:
                    print(f"Error decoding hex data in line: {line.strip()} | Error: {e}")
                continue
            
            # Detect write operations with escaped hex data and 'fd='
            write_escaped_fd_match = write_pattern_escaped_fd.search(line)
            if write_escaped_fd_match:
                fd, hex_str, size = write_escaped_fd_match.groups()
                try:
                    binary_data = binascii.unhexlify(hex_str)
                    if fd in fd_to_file:
                        fd_data[fd].extend(binary_data)
                        print(f"Added {len(binary_data)} bytes to {fd_to_file[fd]} (fd={fd})")
                except binascii.Error as e:
                    print(f"Error decoding escaped hex data in line: {line.strip()} | Error: {e}")
                continue
            
            # Detect write operations with escaped hex data without 'fd='
            write_escaped_match = write_pattern_escaped.search(line)
            if write_escaped_match:
                fd, hex_str, size = write_escaped_match.groups()
                try:
                    binary_data = binascii.unhexlify(hex_str)
                    if fd in fd_to_file:
                        fd_data[fd].extend(binary_data)
                        print(f"Added {len(binary_data)} bytes to {fd_to_file[fd]} (fd={fd})")
                except binascii.Error as e:
                    print(f"Error decoding escaped hex data in line: {line.strip()} | Error: {e}")
                continue
            
            # Detect close operations
            close_match = close_pattern.search(line)
            if close_match:
                fd = close_match.group(1)
                if fd in fd_to_file:
                    filename = fd_to_file.pop(fd)
                    data = fd_data.pop(fd, bytearray())
                    # Check for ELF magic bytes
                    #if data.startswith(b'\x7fELF'):
                    #    output_path = Path(output_dir) / Path(filename).name
                    ##    with open(output_path, 'wb') as out_file:
                    #        out_file.write(data)
                    #    extracted_files.append(str(output_path))
                    #    print(f"Saved ELF file: {output_path} ({len(data)} bytes)")
                    #else:
                    #    print(f"Data written to {filename} does not start with ELF magic bytes. Skipping.")
                    # Always save the file, regardless of magic bytes
                    if data:
                        output_path = Path(output_dir) / Path(filename).name
                        with open(output_path, 'wb') as out_file:
                            out_file.write(data)
                        extracted_files.append(str(output_path))
                        print(f"Saved file: {output_path} ({len(data)} bytes)")
                continue
    
    return extracted_files

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Extract ELF files from strace output.")
    parser.add_argument('strace_file', help="Path to the strace output file.")
    parser.add_argument('-o', '--output', default='extracted_files', help="Directory to save extracted ELF files.")
    
    args = parser.parse_args()
    
    files = extract_elf_files(args.strace_file, args.output)
    if files:
        print("\nExtracted ELF files:")
        for f in files:
            print(f"- {f}")
    else:
        print("\nNo ELF files were extracted.")

Extracted files

Tryed binwalk to no avail.

Extracted with pyinstxtractor

From here it’s just a bunch of nested data, couldn’t get a a script to work, so manual stepped down

The script

_ = lambda __: __import__('base64').b64decode(__[::-1])
decoded_data = (_)(b'==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')

import binascii
print(decoded_data)
#print(binascii.hexlify(decoded_data))

_ = lambda __: __import__('zlib').decompress(__import__('base64').b64decode(__[::-1]))
decoded_data = _(b'=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')
print(decoded_data)

What we need to decode the nesting

_ = lambda **: import('zlib').decompress(import('base64').b64decode(**[::-1]))
exec(_(b'base64 data’))

About half way, the output changed to this

from itertools import cycle as ***;=print;*=''.join;**___*=chr;___________=zip;* = b"";______ = [***^***_ for ***,***_ in zip(**, ______*(*))];(*(***_______(_____) for _____ in ______))

b"# ordlist = [102, 114, 111, 109, 32, 105, 116, 101, 114, 116, 111, 111, 108, 115, 32, 105, 109, 112, 111, 114, 116, 32, 99, 121, 99, 108, 101, 32, 97, 115, 32, 95, 95, 95, 95, 95, 95, 95, 59, 95, 95, 95, 95, 95, 95, 95, 95, 61, 112, 114, 105, 110, 116, 59, 95, 95, 95, 95, 95, 95, 95, 95, 95, 61, 39, 39, 46, 106, 111, 105, 110, 59, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 61, 99, 104, 114, 59, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 61, 122, 105, 112, 59, 95, 32, 61, 32, 98, 34, 92, 120, 54, 52, 92, 120, 51, 53, 92, 120, 51, 48, 92, 120, 54, 50, 92, 120, 54, 52, 92, 120, 51, 49, 92, 120, 51, 48, 92, 120, 51, 54, 92, 120, 51, 48, 92, 120, 51, 52, 92, 120, 51, 56, 92, 120, 51, 53, 92, 120, 54, 53, 92, 120, 51, 54, 92, 120, 54, 51, 92, 120, 51, 50, 92, 120, 51, 52, 92, 120, 51, 56, 92, 120, 51, 52, 92, 120, 51, 56, 92, 120, 54, 50, 92, 120, 54, 54, 92, 120, 51, 56, 92, 120, 51, 51, 92, 120, 54, 53, 92, 120, 54, 54, 92, 120, 51, 50, 92, 120, 51, 55, 92, 120, 54, 49, 92, 120, 51, 56, 92, 120, 51, 50, 92, 120, 54, 54, 92, 120, 51, 52, 92, 120, 51, 57, 92, 120, 51, 49, 92, 120, 54, 54, 92, 120, 54, 54, 92, 120, 54, 52, 92, 120, 51, 56, 92, 120, 54, 49, 92, 120, 51, 54, 92, 120, 54, 50, 92, 120, 54, 49, 92, 120, 54, 53, 92, 120, 51, 48, 92, 120, 51, 53, 92, 120, 54, 54, 92, 120, 54, 52, 92, 120, 51, 54, 92, 120, 51, 56, 92, 120, 51, 54, 92, 120, 51, 49, 92, 120, 51, 54, 92, 120, 51, 48, 92, 120, 51, 53, 92, 120, 54, 52, 92, 120, 51, 54, 92, 120, 51, 53, 92, 120, 51, 57, 92, 120, 51, 49, 92, 120, 51, 55, 92, 120, 51, 50, 92, 120, 51, 55, 92, 120, 51, 55, 92, 120, 54, 54, 92, 120, 51, 48, 92, 120, 51, 52, 92, 120, 51, 54, 92, 120, 51, 51, 92, 120, 54, 49, 92, 120, 51, 49, 92, 120, 54, 49, 92, 120, 51, 55, 92, 120, 51, 53, 92, 120, 51, 55, 92, 120, 51, 55, 92, 120, 51, 51, 92, 120, 51, 56, 92, 120, 51, 53, 92, 120, 54, 49, 92, 120, 51, 50, 92, 120, 51, 54, 92, 120, 51, 50, 92, 120, 54, 51, 92, 120, 51, 55, 92, 120, 54, 53, 92, 120, 54, 54, 92, 120, 54, 49, 92, 120, 51, 49, 92, 120, 54, 52, 92, 120, 54, 49, 92, 120, 51, 51, 92, 120, 51, 53, 92, 120, 51, 49, 92, 120, 51, 48, 92, 120, 51, 53, 92, 120, 51, 52, 92, 120, 54, 53, 92, 120, 51, 53, 92, 120, 54, 54, 92, 120, 54, 50, 92, 120, 51, 52, 92, 120, 54, 54, 92, 120, 51, 50, 92, 120, 51, 50, 92, 120, 54, 49, 92, 120, 54, 53, 92, 120, 51, 52, 92, 120, 51, 53, 92, 120, 54, 53, 92, 120, 54, 52, 92, 120, 54, 50, 92, 120, 54, 53, 92, 120, 54, 49, 92, 120, 51, 56, 92, 120, 51, 56, 92, 120, 51, 53, 92, 120, 54, 54, 92, 120, 54, 49, 92, 120, 51, 57, 92, 120, 51, 57, 92, 120, 54, 54, 92, 120, 54, 51, 92, 120, 54, 51, 92, 120, 54, 51, 92, 120, 54, 49, 92, 120, 54, 51, 92, 120, 51, 50, 34, 59, 95, 95, 32, 61, 32, 98, 34, 92, 120, 51, 98, 92, 120, 49, 53, 92, 120, 48, 100, 92, 120, 52, 50, 92, 120, 48, 56, 92, 120, 53, 48, 92, 120, 53, 100, 92, 120, 53, 52, 92, 120, 53, 52, 92, 120, 53, 53, 92, 120, 49, 56, 92, 120, 54, 97, 92, 120, 51, 97, 92, 120, 49, 54, 92, 120, 53, 57, 92, 120, 49, 50, 92, 120, 54, 98, 92, 120, 54, 55, 92, 120, 53, 100, 92, 120, 53, 53, 92, 120, 49, 50, 92, 120, 48, 57, 92, 120, 52, 97, 92, 120, 52, 55, 92, 120, 51, 97, 92, 120, 51, 57, 92, 120, 49, 97, 92, 120, 49, 48, 92, 120, 49, 98, 92, 120, 53, 52, 92, 120, 53, 98, 92, 120, 48, 52, 92, 120, 49, 51, 92, 120, 49, 48, 92, 120, 49, 102, 92, 120, 48, 50, 92, 120, 48, 51, 92, 120, 48, 55, 92, 120, 53, 55, 92, 120, 48, 99, 92, 120, 52, 54, 92, 120, 49, 48, 92, 120, 48, 52, 92, 120, 49, 54, 92, 120, 52, 51, 92, 120, 49, 100, 92, 120, 51, 57, 92, 120, 51, 98, 92, 120, 53, 102, 92, 120, 53, 53, 92, 120, 52, 54, 92, 120, 53, 101, 92, 120, 52, 52, 92, 120, 52, 52, 92, 120, 54, 97, 92, 120, 51, 98, 92, 120, 49, 101, 92, 120, 49, 50, 92, 120, 53, 98, 92, 120, 53, 48, 92, 120, 52, 52, 92, 120, 53, 55, 92, 120, 48, 49, 92, 120, 48, 51, 92, 120, 52, 49, 92, 120, 49, 57, 92, 120, 49, 97, 92, 120, 53, 52, 92, 120, 48, 53, 92, 120, 53, 53, 92, 120, 53, 53, 92, 120, 48, 52, 92, 120, 53, 52, 92, 120, 53, 97, 92, 120, 53, 51, 92, 120, 53, 50, 92, 120, 49, 98, 92, 120, 54, 55, 92, 120, 54, 97, 92, 120, 51, 97, 92, 120, 48, 56, 92, 120, 48, 99, 92, 120, 49, 102, 92, 120, 53, 50, 92, 120, 54, 97, 92, 120, 52, 99, 92, 120, 52, 102, 92, 120, 53, 97, 92, 120, 53, 52, 92, 120, 49, 99, 92, 120, 48, 52, 92, 120, 53, 48, 92, 120, 49, 100, 92, 120, 49, 57, 92, 120, 54, 102, 92, 120, 49, 99, 92, 120, 49, 99, 92, 120, 48, 55, 92, 120, 49, 50, 92, 120, 53, 98, 92, 120, 53, 102, 92, 120, 52, 51, 92, 120, 49, 52, 92, 120, 54, 52, 92, 120, 52, 49, 92, 120, 51, 50, 92, 120, 50, 52, 92, 120, 49, 98, 92, 120, 48, 53, 92, 120, 52, 97, 92, 120, 52, 98, 92, 120, 52, 100, 92, 120, 53, 99, 92, 120, 51, 98, 92, 120, 49, 51, 92, 120, 52, 57, 92, 120, 48, 48, 92, 120, 50, 56, 92, 120, 50, 52, 92, 120, 54, 56, 92, 120, 54, 100, 92, 120, 53, 54, 92, 120, 48, 52, 92, 120, 52, 56, 92, 120, 53, 48, 92, 120, 53, 55, 92, 120, 50, 52, 92, 120, 55, 57, 92, 120, 48, 49, 92, 120, 55, 102, 92, 120, 48, 54, 92, 120, 49, 52, 92, 120, 50, 100, 92, 120, 55, 55, 92, 120, 53, 56, 92, 120, 53, 57, 92, 120, 48, 52, 92, 120, 53, 50, 92, 120, 54, 100, 92, 120, 52, 102, 92, 120, 50, 54, 92, 120, 53, 51, 92, 120, 53, 48, 92, 120, 48, 55, 92, 120, 55, 100, 92, 120, 48, 49, 92, 120, 52, 48, 92, 120, 53, 101, 92, 120, 48, 101, 92, 120, 49, 99, 92, 120, 52, 56, 92, 120, 48, 55, 92, 120, 53, 50, 92, 120, 51, 50, 92, 120, 48, 54, 92, 120, 53, 51, 92, 120, 53, 55, 92, 120, 54, 101, 92, 120, 53, 55, 92, 120, 48, 48, 92, 120, 52, 54, 92, 120, 55, 101, 92, 120, 53, 97, 92, 120, 51, 48, 92, 120, 49, 48, 92, 120, 51, 55, 92, 120, 54, 99, 92, 120, 53, 56, 92, 120, 55, 50, 92, 120, 53, 52, 92, 120, 53, 53, 92, 120, 50, 99, 92, 120, 55, 51, 92, 120, 53, 99, 92, 120, 50, 55, 92, 120, 50, 99, 92, 120, 54, 51, 92, 120, 54, 50, 92, 120, 53, 100, 92, 120, 48, 55, 92, 120, 55, 50, 92, 120, 53, 57, 92, 120, 53, 50, 92, 120, 53, 49, 92, 120, 52, 99, 92, 120, 55, 49, 92, 120, 55, 55, 92, 120, 52, 56, 92, 120, 48, 53, 92, 120, 53, 56, 92, 120, 53, 102, 92, 120, 55, 97, 92, 120, 49, 52, 92, 120, 48, 56, 92, 120, 48, 53, 92, 120, 52, 102, 92, 120, 54, 97, 92, 120, 50, 56, 92, 120, 48, 50, 92, 120, 48, 100, 92, 120, 52, 50, 92, 120, 53, 52, 92, 120, 54, 102, 92, 120, 55, 99, 92, 120, 53, 101, 92, 120, 53, 54, 92, 120, 49, 101, 92, 120, 49, 55, 92, 120, 49, 100, 92, 120, 52, 99, 92, 120, 53, 97, 92, 120, 51, 54, 92, 120, 54, 53, 92, 120, 49, 53, 92, 120, 48, 57, 92, 120, 50, 100, 92, 120, 53, 97, 92, 120, 48, 98, 92, 120, 50, 51, 92, 120, 48, 97, 92, 120, 48, 50, 92, 120, 52, 57, 92, 120, 53, 98, 92, 120, 53, 48, 92, 120, 55, 100, 92, 120, 49, 54, 92, 120, 52, 52, 92, 120, 49, 53, 92, 120, 53, 52, 92, 120, 55, 98, 92, 120, 48, 102, 92, 120, 54, 55, 92, 120, 54, 51, 92, 120, 51, 48, 92, 120, 48, 102, 92, 120, 55, 101, 92, 120, 52, 53, 92, 120, 48, 48, 92, 120, 53, 54, 92, 120, 50, 48, 92, 120, 50, 51, 92, 120, 48, 101, 92, 120, 53, 98, 92, 120, 55, 55, 92, 120, 55, 52, 92, 120, 50, 57, 92, 120, 48, 56, 92, 120, 55, 102, 92, 120, 53, 98, 92, 120, 48, 49, 92, 120, 50, 101, 92, 120, 48, 101, 92, 120, 51, 53, 92, 120, 48, 99, 92, 120, 51, 51, 92, 120, 55, 55, 92, 120, 51, 53, 92, 120, 53, 50, 92, 120, 48, 49, 92, 120, 49, 98, 92, 120, 48, 50, 92, 120, 55, 48, 92, 120, 53, 53, 92, 120, 52, 99, 92, 120, 52, 57, 92, 120, 54, 53, 92, 120, 53, 97, 92, 120, 53, 49, 92, 120, 52, 97, 92, 120, 54, 52, 92, 120, 52, 56, 92, 120, 54, 49, 92, 120, 54, 54, 92, 120, 54, 48, 92, 120, 53, 49, 92, 120, 52, 97, 92, 120, 53, 48, 92, 120, 49, 48, 92, 120, 55, 100, 92, 120, 54, 97, 92, 120, 51, 100, 92, 120, 49, 99, 92, 120, 53, 51, 92, 120, 53, 98, 92, 120, 53, 56, 92, 120, 55, 102, 92, 120, 53, 101, 92, 120, 48, 52, 92, 120, 52, 50, 92, 120, 55, 97, 92, 120, 55, 102, 92, 120, 48, 57, 92, 120, 49, 99, 92, 120, 48, 48, 92, 120, 52, 102, 92, 120, 49, 54, 92, 120, 48, 52, 92, 120, 48, 100, 92, 120, 50, 52, 92, 120, 51, 52, 92, 120, 52, 97, 92, 120, 53, 97, 92, 120, 50, 48, 92, 120, 52, 102, 92, 120, 48, 50, 92, 120, 54, 101, 92, 120, 55, 99, 92, 120, 52, 53, 92, 120, 55, 49, 92, 120, 48, 55, 92, 120, 55, 54, 92, 120, 51, 99, 92, 120, 53, 101, 92, 120, 48, 55, 92, 120, 52, 57, 92, 120, 52, 52, 92, 120, 53, 102, 92, 120, 52, 54, 92, 120, 53, 99, 92, 120, 53, 98, 92, 120, 48, 57, 92, 120, 55, 52, 92, 120, 49, 98, 92, 120, 49, 57, 92, 120, 55, 101, 92, 120, 50, 101, 92, 120, 54, 48, 92, 120, 48, 52, 92, 120, 55, 50, 92, 120, 52, 54, 92, 120, 54, 100, 92, 120, 52, 102, 92, 120, 54, 57, 92, 120, 54, 99, 92, 120, 48, 99, 92, 120, 49, 54, 92, 120, 54, 53, 92, 120, 55, 97, 92, 120, 53, 98, 92, 120, 50, 50, 92, 120, 54, 49, 92, 120, 53, 52, 92, 120, 49, 99, 92, 120, 50, 53, 92, 120, 54, 53, 92, 120, 53, 100, 92, 120, 50, 102, 92, 120, 53, 97, 92, 120, 55, 48, 92, 120, 55, 99, 92, 120, 54, 57, 92, 120, 53, 100, 92, 120, 52, 100, 92, 120, 49, 51, 92, 120, 49, 97, 92, 120, 52, 100, 92, 120, 53, 52, 92, 120, 53, 55, 92, 120, 50, 48, 92, 120, 52, 50, 92, 120, 52, 98, 92, 120, 50, 98, 92, 120, 48, 99, 92, 120, 55, 54, 92, 120, 55, 51, 92, 120, 51, 54, 92, 120, 49, 99, 92, 120, 50, 55, 92, 120, 51, 49, 92, 120, 51, 54, 92, 120, 54, 97, 92, 120, 54, 98, 92, 120, 54, 50, 92, 120, 50, 49, 92, 120, 50, 52, 92, 120, 53, 102, 92, 120, 49, 50, 92, 120, 48, 56, 92, 120, 53, 98, 92, 120, 52, 99, 92, 120, 48, 52, 92, 120, 48, 52, 92, 120, 48, 98, 92, 120, 55, 53, 92, 120, 50, 48, 92, 120, 53, 54, 92, 120, 53, 53, 92, 120, 51, 55, 92, 120, 50, 49, 92, 120, 55, 48, 92, 120, 54, 52, 92, 120, 53, 57, 92, 120, 55, 49, 92, 120, 55, 99, 92, 120, 54, 50, 92, 120, 52, 54, 92, 120, 48, 97, 92, 120, 55, 48, 92, 120, 50, 52, 92, 120, 55, 56, 92, 120, 53, 102, 92, 120, 52, 48, 92, 120, 55, 100, 92, 120, 53, 102, 92, 120, 48, 102, 92, 120, 51, 54, 92, 120, 52, 56, 92, 120, 53, 100, 92, 120, 53, 53, 92, 120, 50, 101, 92, 120, 53, 49, 92, 120, 52, 102, 92, 120, 48, 54, 92, 120, 49, 55, 92, 120, 48, 98, 92, 120, 53, 52, 92, 120, 55, 102, 92, 120, 52, 56, 92, 120, 55, 57, 92, 120, 48, 100, 92, 120, 52, 57, 92, 120, 48, 101, 92, 120, 54, 49, 92, 120, 49, 52, 92, 120, 54, 99, 92, 120, 53, 55, 92, 120, 49, 52, 92, 120, 53, 50, 92, 120, 52, 48, 92, 120, 55, 51, 92, 120, 48, 52, 92, 120, 50, 51, 92, 120, 53, 57, 92, 120, 55, 51, 92, 120, 53, 98, 92, 120, 55, 52, 92, 120, 52, 52, 92, 120, 54, 48, 92, 120, 52, 102, 92, 120, 53, 55, 92, 120, 53, 49, 92, 120, 52, 51, 92, 120, 54, 97, 92, 120, 55, 101, 92, 120, 53, 49, 92, 120, 53, 99, 92, 120, 48, 54, 92, 120, 55, 49, 92, 120, 48, 57, 92, 120, 55, 97, 92, 120, 55, 53, 92, 120, 55, 55, 92, 120, 49, 99, 92, 120, 51, 50, 92, 120, 53, 51, 92, 120, 48, 48, 92, 120, 53, 56, 92, 120, 55, 50, 92, 120, 55, 99, 92, 120, 53, 51, 92, 120, 53, 49, 92, 120, 54, 102, 92, 120, 53, 98, 92, 120, 51, 55, 92, 120, 55, 48, 92, 120, 49, 57, 92, 120, 52, 97, 92, 120, 50, 102, 92, 120, 48, 102, 92, 120, 50, 49, 92, 120, 51, 101, 92, 120, 50, 50, 92, 120, 55, 97, 92, 120, 50, 56, 92, 120, 51, 56, 92, 120, 55, 56, 92, 120, 53, 57, 92, 120, 55, 50, 92, 120, 53, 102, 92, 120, 55, 98, 92, 120, 52, 48, 92, 120, 51, 53, 92, 120, 53, 97, 92, 120, 51, 101, 92, 120, 48, 97, 92, 120, 52, 101, 92, 120, 49, 99, 92, 120, 53, 55, 92, 120, 49, 100, 92, 120, 49, 48, 92, 120, 51, 49, 92, 120, 53, 97, 92, 120, 55, 56, 92, 120, 49, 53, 92, 120, 53, 54, 92, 120, 51, 98, 92, 120, 49, 53, 92, 120, 53, 52, 92, 120, 55, 57, 92, 120, 55, 54, 92, 120, 55, 101, 92, 120, 50, 52, 92, 120, 49, 98, 92, 120, 55, 102, 92, 120, 55, 50, 92, 120, 48, 55, 92, 120, 49, 51, 92, 120, 52, 56, 92, 120, 48, 99, 92, 120, 48, 56, 92, 120, 50, 102, 92, 120, 49, 100, 92, 120, 51, 52, 92, 120, 48, 53, 92, 120, 55, 53, 92, 120, 50, 100, 92, 120, 53, 52, 92, 120, 55, 52, 92, 120, 54, 97, 92, 120, 54, 55, 92, 120, 52, 48, 92, 120, 48, 51, 92, 120, 53, 54, 92, 120, 55, 101, 92, 120, 48, 56, 92, 120, 48, 49, 92, 120, 48, 99, 92, 120, 53, 54, 92, 120, 49, 98, 92, 120, 55, 102, 92, 120, 53, 50, 92, 120, 48, 48, 92, 120, 50, 57, 92, 120, 53, 51, 92, 120, 52, 97, 92, 120, 53, 101, 92, 120, 53, 99, 92, 120, 48, 48, 92, 120, 52, 97, 92, 120, 54, 55, 92, 120, 53, 51, 92, 120, 55, 49, 92, 120, 48, 55, 92, 120, 50, 57, 92, 120, 53, 53, 92, 120, 53, 51, 92, 120, 55, 54, 92, 120, 48, 101, 92, 120, 52, 100, 92, 120, 51, 51, 92, 120, 53, 97, 92, 120, 48, 99, 92, 120, 54, 52, 92, 120, 51, 53, 92, 120, 51, 53, 92, 120, 48, 57, 92, 120, 54, 48, 92, 120, 54, 55, 92, 120, 51, 55, 92, 120, 50, 48, 92, 120, 54, 51, 92, 120, 48, 100, 92, 120, 53, 48, 92, 120, 54, 48, 92, 120, 48, 54, 92, 120, 55, 101, 92, 120, 54, 48, 92, 120, 49, 55, 92, 120, 53, 53, 92, 120, 55, 99, 92, 120, 55, 55, 92, 120, 52, 56, 92, 120, 52, 50, 92, 120, 55, 57, 92, 120, 55, 97, 92, 120, 52, 50, 92, 120, 48, 97, 92, 120, 55, 100, 92, 120, 53, 54, 92, 120, 55, 97, 92, 120, 54, 50, 92, 120, 52, 101, 92, 120, 53, 51, 92, 120, 51, 52, 92, 120, 55, 102, 92, 120, 55, 55, 92, 120, 53, 51, 92, 120, 53, 50, 92, 120, 55, 101, 92, 120, 55, 98, 92, 120, 55, 50, 92, 120, 48, 56, 92, 120, 52, 48, 92, 120, 54, 53, 92, 120, 55, 54, 92, 120, 51, 49, 92, 120, 55, 53, 92, 120, 50, 56, 92, 120, 50, 48, 92, 120, 50, 97, 92, 120, 49, 101, 92, 120, 53, 99, 92, 120, 50, 100, 92, 120, 55, 55, 92, 120, 55, 51, 92, 120, 48, 53, 92, 120, 52, 97, 92, 120, 48, 99, 92, 120, 55, 97, 92, 120, 50, 55, 92, 120, 53, 101, 92, 120, 48, 48, 92, 120, 49, 49, 92, 120, 48, 50, 92, 120, 50, 102, 92, 120, 48, 49, 92, 120, 53, 100, 92, 120, 50, 102, 92, 120, 53, 52, 92, 120, 52, 52, 92, 120, 49, 97, 92, 120, 48, 51, 92, 120, 51, 52, 92, 120, 51, 52, 92, 120, 50, 55, 92, 120, 53, 57, 92, 120, 55, 54, 92, 120, 55, 97, 92, 120, 49, 97, 92, 120, 48, 57, 92, 120, 49, 50, 92, 120, 53, 49, 92, 120, 52, 99, 92, 120, 52, 100, 92, 120, 49, 52, 92, 120, 51, 52, 92, 120, 50, 54, 92, 120, 48, 50, 92, 120, 49, 97, 92, 120, 54, 56, 92, 120, 48, 102, 92, 120, 52, 50, 92, 120, 54, 52, 92, 120, 50, 101, 92, 120, 51, 101, 92, 120, 55, 101, 92, 120, 52, 55, 92, 120, 55, 101, 92, 120, 55, 49, 92, 120, 52, 99, 92, 120, 48, 48, 92, 120, 54, 53, 92, 120, 49, 55, 92, 120, 55, 100, 92, 120, 48, 99, 92, 120, 53, 53, 92, 120, 55, 51, 92, 120, 48, 98, 92, 120, 55, 97, 92, 120, 48, 97, 92, 120, 49, 56, 92, 120, 49, 49, 92, 120, 53, 57, 92, 120, 52, 50, 92, 120, 49, 53, 92, 120, 51, 54, 92, 120, 48, 53, 92, 120, 48, 51, 92, 120, 49, 51, 92, 120, 53, 49, 92, 120, 52, 51, 92, 120, 50, 51, 92, 120, 53, 54, 92, 120, 52, 51, 92, 120, 55, 54, 92, 120, 48, 53, 92, 120, 53, 53, 92, 120, 49, 52, 92, 120, 55, 53, 92, 120, 49, 55, 92, 120, 54, 102, 92, 120, 51, 53, 92, 120, 48, 53, 92, 120, 48, 57, 92, 120, 53, 50, 92, 120, 55, 57, 92, 120, 49, 51, 92, 120, 49, 55, 92, 120, 54, 102, 92, 120, 55, 52, 92, 120, 52, 53, 92, 120, 55, 53, 92, 120, 53, 56, 92, 120, 53, 53, 92, 120, 55, 49, 92, 120, 50, 97, 92, 120, 55, 50, 92, 120, 48, 52, 92, 120, 53, 51, 92, 120, 52, 98, 92, 120, 55, 99, 92, 120, 48, 53, 92, 120, 55, 49, 92, 120, 55, 101, 92, 120, 49, 49, 92, 120, 52, 48, 92, 120, 54, 101, 92, 120, 52, 53, 92, 120, 53, 55, 92, 120, 48, 54, 92, 120, 52, 50, 92, 120, 52, 101, 92, 120, 53, 48, 92, 120, 54, 50, 92, 120, 48, 50, 92, 120, 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120, 53, 56, 92, 120, 52, 54, 92, 120, 55, 56, 92, 120, 50, 55, 92, 120, 52, 48, 92, 120, 50, 57, 92, 120, 53, 99, 92, 120, 52, 51, 92, 120, 49, 99, 92, 120, 48, 55, 92, 120, 52, 98, 92, 120, 55, 53, 92, 120, 52, 102, 92, 120, 53, 56, 92, 120, 55, 97, 92, 120, 48, 51, 92, 120, 53, 102, 92, 120, 51, 57, 92, 120, 53, 57, 92, 120, 50, 102, 92, 120, 48, 98, 92, 120, 51, 98, 92, 120, 52, 53, 92, 120, 50, 100, 92, 120, 51, 53, 92, 120, 55, 48, 92, 120, 55, 57, 92, 120, 53, 55, 92, 120, 48, 51, 92, 120, 52, 102, 92, 120, 48, 99, 92, 120, 50, 99, 92, 120, 55, 55, 92, 120, 50, 53, 92, 120, 48, 53, 92, 120, 54, 49, 92, 120, 49, 101, 92, 120, 52, 55, 92, 120, 54, 53, 92, 120, 48, 97, 92, 120, 48, 57, 92, 120, 55, 97, 92, 120, 52, 50, 92, 120, 50, 102, 92, 120, 48, 49, 92, 120, 52, 53, 92, 120, 52, 99, 92, 120, 52, 56, 34, 59, 95, 95, 95, 95, 95, 95, 32, 61, 32, 91, 95, 95, 95, 94, 95, 95, 95, 95, 32, 102, 111, 114, 32, 95, 95, 95, 44, 95, 95, 95, 95, 32, 105, 110, 32, 122, 105, 112, 40, 95, 95, 44, 32, 95, 95, 95, 95, 95, 95, 95, 40, 95, 41, 41, 93, 59, 95, 95, 95, 95, 95, 95, 95, 95, 40, 95, 95, 95, 95, 95, 95, 95, 95, 95, 40, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 40, 95, 95, 95, 95, 95, 41, 32, 102, 111, 114, 32, 95, 95, 95, 95, 95, 32, 105, 110, 32, 95, 95, 95, 95, 95, 95, 41, 41]\n# print(''.join(chr(values) for values in ordlist))\nprint('Save your comments about this obfuscation.')"

from itertools import cycle as _______;________=print;_________=''.join;__________=chr;___________=zip;_ = b"\x64\x35\x30\x62\x64\x31\x30\x36\x30\x34\x38\x35\x65\x36\x63\x32\x34\x38\x34\x38\x62\x66\x38\x33\x65\x66\x32\x37\x61\x38\x32\x66\x34\x39\x31\x66\x66\x64\x38\x61\x36\x62\x61\x65\x30\x35\x66\x64\x36\x38\x36\x31\x36\x30\x35\x64\x36\x35\x39\x31\x37\x32\x37\x37\x66\x30\x34\x36\x33\x61\x31\x61\x37\x35\x37\x37\x33\x38\x35\x61\x32\x36\x32\x63\x37\x65\x66\x61\x31\x64\x61\x33\x35\x31\x30\x35\x34\x65\x35\x66\x62\x34\x66\x32\x32\x61\x65\x34\x35\x65\x64\x62\x65\x61\x38\x38\x35\x66\x61\x39\x39\x66\x63\x63\x63\x61\x63\x32";__ = 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= [___^____ for ___,____ in zip(__, _______(_))];________(_________(__________(_____) for _____ in ______))

Continue stepping down the nested data to print out the flag. Tedious in short.

_ = lambda __: __import__('zlib').decompress(__import__('base64').b64decode(__[::-1]))
decoded_data = _(b'==QtSMFZA4STl0DVkzL/FSF3FyB/8fbJNIBvMLqguq1wUt8STN1UzM3M0IqAxIDTORjSIVjML5ENPVzMzkkNvyUSLjkVTxJe')
print(decoded_data)

flag{4f657a3fb50ba3ab42b42f67655599e1}