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
import markovify 
import config
import random
from twitter_scraper_fetcher import get_user_tweets as scrape
from better_profanity import profanity
import re

profanity.load_censor_words()

# remove emoji, punctuation, urls from tweets
def clean_tweets_data(tweets):
    text = ""

    # remove emoji from tweets:
    emoji_pattern = re.compile(
        "["
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
        "]+",
        flags=re.UNICODE,
    )
    url_pattern = re.compile(r"\S+\/\S+", re.DOTALL)
    mentions_pattern = re.compile(r"@\S+", re.DOTALL)
    # return tweet
    for tweet in tweets:
        # workaround to get full_text from tweepy (otherwise text is truncated)
        text_without_emoji = emoji_pattern.sub(r"", tweet)
        text_without_url = url_pattern.sub(r"", text_without_emoji)
        cleaned_text = mentions_pattern.sub(r"", text_without_url)
        text += (
            profanity.censor(text_without_emoji) + "\n\n"
        )  # Make sure each tweet is handled properly by markovify

    return text


def generate_bot_answer_with_text_model(twitter_handle, user_question, text_model):
    bot_answer = None

    for word in user_question.split():
        try:
            #picks random tweet from list of tweets
            bot_answer = random.choice(tweets);
        except:
            pass

        if bot_answer != None:
            break

    if bot_answer == None:
        bot_answer = text_model.make_sentence(test_output=False)

    return (
        bot_answer
        if bot_answer != None
        else generate_bot_answer_with_text_model(
            twitter_handle, user_question, text_model
        )
    )


# build the markov chain based on the text we read
# we use the markovify library to do this step
def generate_bot_answer(twitter_handle, user_question):
    tweets = (
        scrape(twitter_handle)
        if config.TWITTER_FETCHER == "scraper"
        else fetch(twitter_handle)
    )
    
    text_model = markovify.Text(clean_tweets_data(tweets))
    return generate_bot_answer_with_text_model(
        twitter_handle, user_question, text_model
    )

```

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

  A) CWE-20: Improper Input Validation
  B) CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  C) CWE-614: Sensitive Cookie in HTTPS Session Without 'Secure' Attribute
  D) CWE-377: Insecure Temporary File
  E) CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  F) CWE-327: Use of a Broken or Risky Cryptographic Algorithm
Answer: A