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
# --------------
# import packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings("ignore")

# Load the dataset
df = pd.read_csv(path,sep="\t")
df.info()
# Converting date attribute from string to datetime.date datatype 
df['date'] = pd.to_datetime(df['date'])

# calculate the total length of word
length = []
leng=0
for row in df['verified_reviews']:
    leng = len(row)
    length.append(leng)

df['length'] = length


# --------------
## Rating vs feedback

# set figure size
plt.figure(figsize=(5,7))
# generate countplot
sns.countplot(x='rating',hue='feedback',data=df)
# display plot
plt.show()
## Product rating vs feedback

# set figure size


# generate barplot
sns.barplot(x='rating',y='variation',hue='feedback',data=df)

# display plot
plt.show()



# --------------
# import packages
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

# declare empty list 'corpus'
corpus = []

# for loop to fill in corpus
for i in range(0,3150):
    # retain alphabets
    review=[]
    review = re.sub('[^a-zA-z\s]','',df['verified_reviews'][i])
    # convert to lower case
    review = review.lower()
    # tokenize
    review = review.split()
    # initialize stemmer object
    ps = PorterStemmer()
    # perform stemming
    stop = stopwords.words('english')
    review = [x for x in review if x not in stop]
    review = [ps.stem(i) for i in review ]

    # join elements of list
    review = ' '.join(review)
    
    # add to 'corpus'
    corpus.append(review)
# display 'corpus'
print(corpus)


# --------------
# import libraries
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split

# Instantiate count vectorizer
cv = CountVectorizer(max_features=1500)

# Independent variable
X = cv.fit_transform(corpus).toarray()

# dependent variable
y = df['feedback']
# Counts
count = y.value_counts()
# Split the dataset
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
X_train.shape


# --------------
# import packages
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, f1_score

# Instantiate classifier
rf = RandomForestClassifier(random_state=2)
# fit model on training data
rf.fit(X_train,y_train)
# predict on test data
y_pred = rf.predict(X_test)
# calculate the accuracy score
score = accuracy_score(y_test,y_pred)
# calculate the precision
precision = precision_score(y_test,y_pred)
# display 'score' and 'precision'
print(score,precision)
precision = 0.947107438016529


# --------------
# import packages
from imblearn.over_sampling import SMOTE

# Instantiate smote
smote = SMOTE(random_state=9)
# fit_sample onm training data
X_train,y_train = smote.fit_sample(X_train,y_train)
# fit modelk on training data
rf.fit(X_train,y_train)
# predict on test data
y_pred = rf.predict(X_test)
# calculate the accuracy score
score = accuracy_score(y_test,y_pred)
# calculate the precision
precision = precision_score(y_test,y_pred)
# display precision and score
print(score,precision)
score = 0.8904761904761904 
precision = 0.9439579684763573




```

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