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 packages
import sys
import pandas as pd
from sqlalchemy import create_engine
import numpy as np
import re
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.multioutput import MultiOutputClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
import pickle

# download necessary NLTK data
import nltk
nltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger', 'stopwords'])

import warnings
warnings.filterwarnings('ignore')


def load_data(database_filepath):
    """
    load data from ETL pipeline
    Args:
    input_file: first argument passed to script
    """
    engine = create_engine('sqlite:///' + database_filepath)
    df = pd.read_sql('messages', engine)
    X = df['message']
    y = df.drop(columns=['id', 'message', 'genre'])
    return X, y

def evaluate_model(model, X_test, y_test):
    """
    evaluation precision, recall, accuracy, f1_score for each category and whole model
    Args:
    model: tuned model
    X_test: X test set
    y_test: y test set
    """
    multi_predictions = pd.DataFrame(model.predict(X_test))
    multi_predictions.columns = y_test.columns.copy()

    eval_list = []
    for column in multi_predictions:
        # set each value to be the last character of the string
        #confusion_mat = confusion_matrix(y_test[column], multi_predictions[column])
        report = classification_report(y_test[column],multi_predictions[column])
        accuracy = accuracy_score(y_test[column],multi_predictions[column])
        precision = precision_score(y_test[column],multi_predictions[column], average='weighted')
        recall = recall_score(y_test[column],multi_predictions[column], average='weighted')
        f1 = f1_score(y_test[column],multi_predictions[column], average='weighted')
        print("Label:", column)
        print(report)
        eval_list.append([precision, recall, accuracy, f1])
        #precision_list.append(precision)
        #recall_list.append(recall)
        #f1_list.append(f1)

        print("-----------------------------------------------------------------------")

    evaluation = pd.DataFrame(eval_list)
    evaluation.columns = ['precision','recall','accuracy','f1_score']
    #evaluation['recall'] = recall_list
    #evaluation['accuracy'] = accuracy_list
    #evaluation['f1_score'] = f1_list
    print(evaluation)
    print("*******Overall Evaluation*******\nPrecision:{:.2f}\tRecall:{:.2f}\nAccuracy:{:.2f}\tF1 Score:{:.2f}".format(
        np.mean(evaluation.precision), np.mean(evaluation.recall),
        np.mean(evaluation.accuracy), np.mean(evaluation.f1_score)))

    return evaluation



def tokenize(text):
    """
    a tokenization function to process your text data
    Args:
    text: text
    """
    url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
    detected_urls = re.findall(url_regex, text)
    for url in detected_urls:
        text = text.replace(url, "urlplaceholder")

    tokens = word_tokenize(text)
    lemmatizer = WordNetLemmatizer()
    # remove stop words
    STOPWORDS = stopwords.words("english")
    tokens = [word for word in tokens if word not in STOPWORDS]

    clean_tokens = []
    for tok in tokens:
        clean_tok = lemmatizer.lemmatize(tok).lower().strip()
        clean_tokens.append(clean_tok)

    return clean_tokens

def main():
    """
    main function: Receive two attributes at run time
    Args
    Arg1: Input DB file name along path example- /home/filename.db
    Arg2: Output pickle file name along with path example- /home/model.sav
    """
    if len(sys.argv) == 3:
        database_filepath, model_filepath = sys.argv[1:]
        print('Loading data...\n    DATABASE: {}'.format(database_filepath))
        X, y = load_data(database_filepath)

        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=101)
        pipeline = Pipeline([('vect', CountVectorizer(tokenizer=tokenize)),
                           ('tfidf', TfidfTransformer()),
                           ('clf', MultiOutputClassifier(OneVsRestClassifier(LinearSVC())))])
        print('Building model...')
        parameters = {'vect__ngram_range': ((1, 1), (1, 2)),
                'vect__max_df': (0.75, 1.0),
                'tfidf__use_idf': (True, False)
                }

        cross_validation = GridSearchCV(pipeline, param_grid=parameters, verbose = 3, n_jobs=-1)


        # fit model
        print('Training model...')
        model_tuned = cross_validation.fit(X_train, y_train)
        # output model test results
        print('Evaluating model...')
        the_evaluation = evaluate_model(model_tuned, X_test, y_test)

        print('Saving model...\n    MODEL: {}'.format(model_filepath))
        #model_tuned = train(X, y, model)  # train model pipeline
        # Export model as a pickle file
        pickle.dump(model_tuned, open(model_filepath, 'wb')) # save model
        print('Trained model saved!')


    else:
        print('Please provide the filepath of the disaster messages database '\
              'as the first argument and the filepath of the pickle file to '\
              'save the model to as the second argument. \n\nExample: python '\
              'train_classifier.py ../data/DisasterResponse.db classifier.pkl')



if __name__ == '__main__':
    main()

```

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-1333: Inefficient Regular Expression Complexity
  C) CWE-601: URL Redirection to Untrusted Site ('Open Redirect')
  D) CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  E) CWE-776: Improper Restriction of Recursive Entity References in DTDs ('XML Entity Expansion')
  F) CWE-209: Generation of Error Message Containing Sensitive Information
Answer: A