system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
# -*- coding: utf-8 -*-
# 모델 구현을 위한 유틸리티 함수들

import tensorflow as tf
import numpy as np
import scipy.misc as misc
import os, sys
from six.moves import urllib
import tarfile
import zipfile
import scipy.io

# VGGNet 파라미터가 저장된 mat 파일을 다운로드 받고 불러옵니다.
def get_model_data(dir_path, model_url):
  maybe_download_and_extract(dir_path, model_url)
  filename = model_url.split("/")[-1]
  filepath = os.path.join(dir_path, filename)
  if not os.path.exists(filepath):
      raise IOError("VGG Model not found!")
  data = scipy.io.loadmat(filepath)
  return data

# dir_path에 url_name에서 다운받은 zip파일의 압축을 해제합니다.
def maybe_download_and_extract(dir_path, url_name, is_tarfile=False, is_zipfile=False):
  if not os.path.exists(dir_path):
    os.makedirs(dir_path)
  filename = url_name.split('/')[-1]
  filepath = os.path.join(dir_path, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write(
          '\r>> Downloading %s %.1f%%' % (filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()

    filepath, _ = urllib.request.urlretrieve(url_name, filepath, reporthook=_progress)
    print()
    statinfo = os.stat(filepath)
    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
    if is_tarfile:
      tarfile.open(filepath, 'r:gz').extractall(dir_path)
    elif is_zipfile:
      with zipfile.ZipFile(filepath) as zf:
        zip_dir = zf.namelist()[0]
        zf.extractall(dir_path)

# 이미지를 png 파일로 저장합니다.
def save_image(image, save_dir, name, mean=None):
  """
  만약 평균값을 argument로 받으면 평균값을 더한뒤에 이미지를 저장하고, 아니면 바로 이미지를 저장합니다.
  """
  if mean:
    image = unprocess_image(image, mean)
  misc.imsave(os.path.join(save_dir, name + ".png"), image)

# 변수를 선언합니다.
def get_variable(weights, name):
  init = tf.constant_initializer(weights, dtype=tf.float32)
  var = tf.get_variable(name=name, initializer=init, shape=weights.shape)
  return var

# weight를 선언합니다.
def weight_variable(shape, stddev=0.02, name=None):
  initial = tf.truncated_normal(shape, stddev=stddev)
  if name is None:
    return tf.Variable(initial)
  else:
    return tf.get_variable(name, initializer=initial)

# bias를 선언합니다.
def bias_variable(shape, name=None):
  initial = tf.constant(0.0, shape=shape)
  if name is None:
    return tf.Variable(initial)
  else:
    return tf.get_variable(name, initializer=initial)

# Convolution을 정의합니다.
def conv2d_basic(x, W, bias):
  conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
  return tf.nn.bias_add(conv, bias)

# Deconvolution(Transpose Convolution)을 정의합니다.
def conv2d_transpose_strided(x, W, b, output_shape=None, stride = 2):
  # print x.get_shape()
  # print W.get_shape()
  if output_shape is None:
    output_shape = x.get_shape().as_list()
    output_shape[1] *= 2
    output_shape[2] *= 2
    output_shape[3] = W.get_shape().as_list()[2]
  # print output_shape
  conv = tf.nn.conv2d_transpose(x, W, output_shape, strides=[1, stride, stride, 1], padding="SAME")
  return tf.nn.bias_add(conv, b)

# 2x2 max 풀링을 정의합니다.
def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

# 2x2 average 풀링을 정의합니다.
def avg_pool_2x2(x):
  return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

# 이미지에 평균을 뺍니다.
def process_image(image, mean_pixel):
  return image - mean_pixel

# 이미지에 평균을 더합니다.
def unprocess_image(image, mean_pixel):
  return image + mean_pixel
```

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
  B) CWE-209: Generation of Error Message Containing Sensitive Information
  C) CWE-295: Improper Certificate Validation
  D) CWE-611: Improper Restriction of XML External Entity Reference
  E) CWE-312: Cleartext Storage of Sensitive Information
  F) CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Answer: F