system: You are an expert security code reviewer. You will be shown a source code file along with a catalog of candidate CWE (Common Weakness Enumeration) categories with their official MITRE names. Your task is to list every distinct CWE from that catalog that is actually present in the code. The catalog is comprehensive for this dataset -- the answer is a (possibly small) subset of it.

OUTPUT FORMAT: Respond with a comma-separated list of CWE IDs in the form `CWE-N`, sorted in ascending numeric order, wrapped in a single \boxed{...}. Examples: `\boxed{CWE-79}` for one, `\boxed{CWE-22,CWE-79,CWE-327}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
import re
import string
import random
from nltk.tokenize import WordPunctTokenizer
from nltk.tag import pos_tag
from nltk.stem.wordnet import WordNetLemmatizer 
from nltk.corpus import stopwords


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from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn import preprocessing


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from google.colab import files 
uploaded = files.upload()


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def extract_csv():
    my_filtered_csv = pd.read_csv('clown_1.csv', usecols=['tweet'])
    return my_filtered_csv

def tokenize_tweets(clown_1) :
    tweets = clown_1.tweet.tolist()
    tokenizer = WordPunctTokenizer() 
    cleaned = []
    for i in range(0, len(tweets)):
        text = tweets[i]
        text = re.sub('^https?://.*[rn]*','', text)
        text = re.sub(r'\w+:\/{2}[\d\w-]+(\.[\d\w-]+)*(?:(?:\/[^\s/]*))*', '', text)
        text = re.sub("(@[A-Za-z0-9_]+)","", text)
        text = re.sub("([^\w\s])", "", text)
        text = re.sub("^RT", "", text)
        text = tokenizer.tokenize(text)
        element = [text]
        cleaned.append(element)
    return cleaned

def lemmatize_sentence(tweet_tokens, stop_words = ()):
    lemmatizer = WordNetLemmatizer()
    cleaned_tokens = []
    for token, tag in pos_tag(tweet_tokens):
        if tag.startswith('NN'):
            pos = 'n'
        elif tag.startswith('V'):
            pos = 'v'
        else:
            pos = 'a'
        token = lemmatizer.lemmatize(token, pos)
        if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:
            cleaned_tokens.append(token.lower())
    return cleaned_tokens

def create_lemmatized_sent(words):
    cleaned = []
    stop_words = stopwords.words('english')
    for i in range(0, len(words)):
        sent = lemmatize_sentence(words[i][0], stop_words)
        if len(sent) >= 0:
            element = [sent]
            cleaned.append(element)
    return cleaned

def remove_emoji(string):
    emoji_pattern = re.compile("["
                           u"\U0001F600-\U0001F64F"  # emoticons
                           u"\U0001F300-\U0001F5FF"  # symbols & pictographs
                           u"\U0001F680-\U0001F6FF"  # transport & map symbols
                           u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                           u"\U00002702-\U000027B0"
                           u"\U000024C2-\U0001F251"
                           "]+", flags=re.UNICODE)
    return emoji_pattern.sub(r'', string)

def write_sent(sent):
    cleaned = []
    for i in sent:
        s = ""
        for j in i[0]:
            j = str(j)
            j = j + " "
            s = s + j
        s = remove_emoji(s)
        element = [s]
        cleaned.append(element)
    df = pd.DataFrame(cleaned)
    # print(df.iloc[1])
    #df.to_csv('cleaned_clown_1.csv', index=False)
    df1 = pd.read_csv('clown_1.csv')
    df1 = df1['time']
    big = pd.concat([df, df1], axis = 1)
    big.to_csv('cleaned_clown_1.csv', index=False)

clown_1 = extract_csv()
words = tokenize_tweets(clown_1)
sent = create_lemmatized_sent(words)
write_sent(sent)


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df = pd.read_csv('cleaned_clown_1.csv')
df = df.rename(columns={"0": "clean_tweet"})
df


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df.to_csv('cleaned_clown_1.csv', index=True) 
files.download('cleaned_clown_1.csv')


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```

Candidate CWE catalog (every CWE that appears anywhere in this dataset):
  - CWE-20: Improper Input Validation
  - CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
  - CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
  - CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
  - CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
  - CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
  - CWE-94: Improper Control of Generation of Code ('Code Injection')
  - CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  - CWE-116: Improper Encoding or Escaping of Output
  - CWE-117: Improper Output Neutralization for Logs
  - CWE-200: Exposure of Sensitive Information to an Unauthorized Actor
  - CWE-209: Generation of Error Message Containing Sensitive Information
  - CWE-215: Insertion of Sensitive Information Into Debugging Code
  - CWE-295: Improper Certificate Validation
  - CWE-312: Cleartext Storage of Sensitive Information
  - CWE-326: Inadequate Encryption Strength
  - CWE-327: Use of a Broken or Risky Cryptographic Algorithm
  - CWE-377: Insecure Temporary File
  - CWE-502: Deserialization of Untrusted Data
  - CWE-601: URL Redirection to Untrusted Site ('Open Redirect')
  - CWE-611: Improper Restriction of XML External Entity Reference
  - CWE-614: Sensitive Cookie in HTTPS Session Without 'Secure' Attribute
  - CWE-643: Improper Neutralization of Data within XPath Expressions ('XPath Injection')
  - CWE-730: OWASP Top Ten 2004 Category A9 - Denial of Service
  - CWE-732: Incorrect Permission Assignment for Critical Resource
  - CWE-776: Improper Restriction of Recursive Entity References in DTDs ('XML Entity Expansion')
  - CWE-918: Server-Side Request Forgery (SSRF)
  - CWE-943: Improper Neutralization of Special Elements in Data Query Logic
  - CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  - CWE-1333: Inefficient Regular Expression Complexity

List every distinct CWE from the catalog above that is actually present in this code.
Answer: CWE-20