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129 changes: 129 additions & 0 deletions new-test/app.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,129 @@
from flask import Flask, request, jsonify
from flask_sqlalchemy import SQLAlchemy
import jwt
import os
import pandas as pd
import numpy as np
from datetime import datetime
import redis
import json
import subprocess
import base64
import hashlib
import requests
import time
import xml.etree.ElementTree as ET

# Unused imports (dead code)
import logging
import threading
import socket

app = Flask(__name__)

# Security Issue: Hardcoded credentials
DB_PASSWORD = "super_secret_password123"
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🚨 issue (security): Hardcoded password detected.

JWT_SECRET = "my_jwt_secret_key"
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🚨 issue (security): Hardcoded JWT secret detected.

Comment on lines +25 to +26
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Suggestion: Replace hardcoded credentials with environment variable lookups. [security]

Suggested change
DB_PASSWORD = "super_secret_password123"
JWT_SECRET = "my_jwt_secret_key"
DB_PASSWORD = os.getenv("DB_PASSWORD")
JWT_SECRET = os.getenv("JWT_SECRET")


Comment on lines +24 to +27
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⚠️ Potential issue

Avoid hardcoding sensitive credentials.

Storing critical secrets like database passwords and JWT secrets in plain text can lead to security breaches. Consider using environment variables or a secure secrets manager.

# Security Issue: Insecure database configuration
app.config['SQLALCHEMY_DATABASE_URI'] = f'sqlite:///app.db'
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suggestion (code-quality): Replace f-string with no interpolated values with string (remove-redundant-fstring)

Suggested change
app.config['SQLALCHEMY_DATABASE_URI'] = f'sqlite:///app.db'
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'

Comment on lines +28 to +29
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⚠️ Potential issue

Insecure database configuration and extraneous f-string prefix.

  1. The database URI uses an f-string but does not interpolate any variable. Remove the f prefix.
  2. SQLite is fine for demos, but storing credentials in code plus referencing them is risky; ensure you handle credentials properly if you upgrade to a production-quality DB.
-app.config['SQLALCHEMY_DATABASE_URI'] = f'sqlite:///app.db'
+app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
# Security Issue: Insecure database configuration
app.config['SQLALCHEMY_DATABASE_URI'] = f'sqlite:///app.db'
# Security Issue: Insecure database configuration
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'
🧰 Tools
🪛 Ruff (0.8.2)

29-29: f-string without any placeholders

Remove extraneous f prefix

(F541)

app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False

db = SQLAlchemy(app)
redis_client = redis.Redis(host='localhost', port=6379, db=0)

# Inefficient data structure: Using list instead of set for O(n) lookup
BLOCKED_IPS = []

class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(80), unique=True, nullable=False)
password = db.Column(db.String(120), nullable=False)
documents = db.relationship('Document', backref='owner', lazy=True)

class Document(db.Model):
id = db.Column(db.Integer, primary_key=True)
content = db.Column(db.Text, nullable=False)
user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)

# Performance Issue: Unnecessary computation in loop
def process_user_data(users):
result = []
for user in users:
# Inefficient: Recalculating same value in loop
timestamp = int(time.time())
processed_data = {
'id': user.id,
'username': user.username,
'docs_count': len(user.documents),
'timestamp': timestamp
}
# Unnecessary list conversion
result = result + [processed_data]
return result

# Security Issue: SQL Injection vulnerability
@app.route('/search_users')
def search_users():
query = request.args.get('q', '')
# NEVER do this in real code - SQL injection vulnerability
raw_sql = f"SELECT * FROM user WHERE username LIKE '%{query}%'"
result = db.engine.execute(raw_sql)
Comment on lines +70 to +71
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Suggestion: Use parameterized queries to safely incorporate user input in SQL statements. [security]

Suggested change
raw_sql = f"SELECT * FROM user WHERE username LIKE '%{query}%'"
result = db.engine.execute(raw_sql)
from sqlalchemy import text
raw_sql = text("SELECT * FROM user WHERE username LIKE :query")
result = db.engine.execute(raw_sql, query=f"%{query}%")

return jsonify([dict(row) for row in result])

Comment on lines +65 to +73
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⚠️ Potential issue

Prevent SQL injection by using parameterized queries.

Constructing SQL strings with unsanitized input opens the door to attackers. Always use parameter binding or ORM methods.

- raw_sql = f"SELECT * FROM user WHERE username LIKE '%{query}%'"
- result = db.engine.execute(raw_sql)
+ # Example of using a safe parameter for LIKE queries:
+ raw_sql = text("SELECT * FROM user WHERE username LIKE :query")
+ result = db.engine.execute(raw_sql, {"query": f"%{query}%"})
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
# Security Issue: SQL Injection vulnerability
@app.route('/search_users')
def search_users():
query = request.args.get('q', '')
# NEVER do this in real code - SQL injection vulnerability
raw_sql = f"SELECT * FROM user WHERE username LIKE '%{query}%'"
result = db.engine.execute(raw_sql)
return jsonify([dict(row) for row in result])
# Security Issue: SQL Injection vulnerability
@app.route('/search_users')
def search_users():
query = request.args.get('q', '')
# Example of using a safe parameter for LIKE queries:
raw_sql = text("SELECT * FROM user WHERE username LIKE :query")
result = db.engine.execute(raw_sql, {"query": f"%{query}%"})
return jsonify([dict(row) for row in result])

# Security Issue: Command Injection vulnerability
@app.route('/ping')
def ping_host():
host = request.args.get('host', 'localhost')
# NEVER do this in real code - Command injection vulnerability
result = subprocess.check_output(f'ping -c 1 {host}', shell=True)
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Suggestion: Prevent command injection by eliminating shell=True and passing command arguments as a list. [security]

Suggested change
result = subprocess.check_output(f'ping -c 1 {host}', shell=True)
result = subprocess.check_output(['ping', '-c', '1', host])

return result.decode()

Comment on lines +74 to +81
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⚠️ Potential issue

Mitigate command injection by sanitizing or restricting user input.

Running shell commands directly with user input is extremely risky. Consider safer alternatives, like Python’s built-in ping libraries or verifying trusted host inputs.

- result = subprocess.check_output(f'ping -c 1 {host}', shell=True)
- return result.decode()
+ # Example of a safer approach:
+ import shlex
+ safe_host = shlex.quote(host)
+ result = subprocess.check_output(["ping", "-c", "1", safe_host])
+ return result.decode()
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
# Security Issue: Command Injection vulnerability
@app.route('/ping')
def ping_host():
host = request.args.get('host', 'localhost')
# NEVER do this in real code - Command injection vulnerability
result = subprocess.check_output(f'ping -c 1 {host}', shell=True)
return result.decode()
# Security Issue: Command Injection vulnerability
@app.route('/ping')
def ping_host():
host = request.args.get('host', 'localhost')
# NEVER do this in real code - Command injection vulnerability
# Example of a safer approach:
import shlex
safe_host = shlex.quote(host)
result = subprocess.check_output(["ping", "-c", "1", safe_host])
return result.decode()

# Performance Issue: Inefficient data processing
@app.route('/process_documents', methods=['POST'])
def process_documents():
documents = request.json.get('documents', [])

# Inefficient: Creating new DataFrame for each document
results = []
for doc in documents:
df = pd.DataFrame([doc])
# Unnecessary type conversion
doc_id = str(doc.get('id'))
doc_id = int(doc_id)

# Memory inefficient: Creating new array for each operation
data = np.array(df.values)
data = data * 2
data = data + 1
results.append(data.tolist())

return jsonify(results)

Comment on lines +82 to +102
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🛠️ Refactor suggestion

Streamline document processing to improve performance.

  1. Creating a new DataFrame for each document can be expensive.
  2. Repeated type conversions and array allocations may consume excessive memory.

Below is a sketch showing how you might handle all documents in a single DataFrame:

-def process_documents():
-    documents = request.json.get('documents', [])
-    results = []
-    for doc in documents:
-        df = pd.DataFrame([doc])
-        doc_id = str(doc.get('id'))
-        doc_id = int(doc_id)
-        data = np.array(df.values)
-        data = data * 2
-        data = data + 1
-        results.append(data.tolist())
-    return jsonify(results)
+def process_documents():
+    docs_json = request.json.get('documents', [])
+    df = pd.DataFrame(docs_json)
+    # Example vectorized transformations:
+    df['id'] = df['id'].astype(int)
+    numeric_data = df.select_dtypes(include=[np.number]) * 2 + 1
+    df.update(numeric_data)
+    return jsonify(df.to_dict(orient='records'))
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‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
# Performance Issue: Inefficient data processing
@app.route('/process_documents', methods=['POST'])
def process_documents():
documents = request.json.get('documents', [])
# Inefficient: Creating new DataFrame for each document
results = []
for doc in documents:
df = pd.DataFrame([doc])
# Unnecessary type conversion
doc_id = str(doc.get('id'))
doc_id = int(doc_id)
# Memory inefficient: Creating new array for each operation
data = np.array(df.values)
data = data * 2
data = data + 1
results.append(data.tolist())
return jsonify(results)
# Performance Issue: Inefficient data processing
@app.route('/process_documents', methods=['POST'])
def process_documents():
docs_json = request.json.get('documents', [])
df = pd.DataFrame(docs_json)
# Example vectorized transformations:
df['id'] = df['id'].astype(int)
numeric_data = df.select_dtypes(include=[np.number]) * 2 + 1
df.update(numeric_data)
return jsonify(df.to_dict(orient='records'))

# Security Issue: XML parsing vulnerability
@app.route('/parse_xml', methods=['POST'])
def parse_xml():
xml_data = request.data
# NEVER do this in real code - XML parsing vulnerability
root = ET.fromstring(xml_data)
return jsonify({'root_tag': root.tag})

Comment on lines +103 to +110
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⚠️ Potential issue

Validate or sanitize XML input to prevent XXE and other XML vulnerabilities.

Blindly parsing user-supplied XML can allow harmful payloads. Use secure parsers or implement checks to guard against malicious entities (e.g., disable external entity references).

# Business Logic Error: Incorrect calculation
def calculate_discount(price, quantity):
# Error: Applies discount incorrectly
if quantity > 10:
return price * 0.9 # Should be (price * quantity) * 0.9
return price
Comment on lines +114 to +116
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suggestion (code-quality): We've found these issues:

Suggested change
if quantity > 10:
return price * 0.9 # Should be (price * quantity) * 0.9
return price
return price * 0.9 if quantity > 10 else price

Comment on lines +113 to +116
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Suggestion: Correct the discount calculation to apply the discount to the total price for quantities over 10. [possible bug]

Suggested change
# Error: Applies discount incorrectly
if quantity > 10:
return price * 0.9 # Should be (price * quantity) * 0.9
return price
def calculate_discount(price, quantity):
if quantity > 10:
return (price * quantity) * 0.9
return price * quantity


Comment on lines +111 to +117
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⚠️ Potential issue

Fix incorrect discount logic.

The discount calculation is likely intended to apply to the total cost of the items, not just the unit price.

-def calculate_discount(price, quantity):
-    if quantity > 10:
-        return price * 0.9
-    return price
+def calculate_discount(price, quantity):
+    if quantity > 10:
+        return (price * quantity) * 0.9
+    return price * quantity
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
# Business Logic Error: Incorrect calculation
def calculate_discount(price, quantity):
# Error: Applies discount incorrectly
if quantity > 10:
return price * 0.9 # Should be (price * quantity) * 0.9
return price
# Business Logic Error: Incorrect calculation
def calculate_discount(price, quantity):
# Error: Applies discount incorrectly
if quantity > 10:
return (price * quantity) * 0.9
return price * quantity

# Dead Code: Never used function
def unused_helper_function():
print("This function is never called")

# Security Issue: Insecure password hashing
def hash_password(password):
# NEVER do this in real code - Use proper password hashing
return hashlib.md5(password.encode()).hexdigest()
Comment on lines +123 to +125
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Suggestion: Replace MD5 with a secure password hashing algorithm like bcrypt. [security]

Suggested change
def hash_password(password):
# NEVER do this in real code - Use proper password hashing
return hashlib.md5(password.encode()).hexdigest()
def hash_password(password):
return bcrypt.hashpw(password.encode(), bcrypt.gensalt()).decode()


Comment on lines +122 to +126
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⚠️ Potential issue

Use a secure password hashing function.

MD5 is cryptographically broken and unsuitable for passwords. Switch to a robust library-based approach such as bcrypt or argon2.

-def hash_password(password):
-    return hashlib.md5(password.encode()).hexdigest()
+import bcrypt
+
+def hash_password(password):
+    salt = bcrypt.gensalt()
+    return bcrypt.hashpw(password.encode(), salt).decode()
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
# Security Issue: Insecure password hashing
def hash_password(password):
# NEVER do this in real code - Use proper password hashing
return hashlib.md5(password.encode()).hexdigest()
# Security Issue: Insecure password hashing
import bcrypt
def hash_password(password):
salt = bcrypt.gensalt()
return bcrypt.hashpw(password.encode(), salt).decode()

if __name__ == '__main__':
# Security Issue: Debug mode in production
app.run(debug=True, host='0.0.0.0', port=5000)
Comment on lines +127 to +129
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⚠️ Potential issue

Disable debug mode in production.

Running the Flask app with debug=True reveals sensitive tracebacks and can further expose your system. Set debug=False or use configuration files/environment variables to toggle this safely.

-if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=5000)
+if __name__ == '__main__':
+    app.run(debug=False, host='0.0.0.0', port=5000)
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
if __name__ == '__main__':
# Security Issue: Debug mode in production
app.run(debug=True, host='0.0.0.0', port=5000)
if __name__ == '__main__':
app.run(debug=False, host='0.0.0.0', port=5000)
🧰 Tools
🪛 ast-grep (0.31.1)

[warning] 128-128: Detected Flask app with debug=True. Do not deploy to production with this flag enabled as it will leak sensitive information. Instead, consider using Flask configuration variables or setting 'debug' using system environment variables.
Context: app.run(debug=True, host='0.0.0.0', port=5000)
Note: [CWE-489] Active Debug Code. [REFERENCES]
- https://labs.detectify.com/2015/10/02/how-patreon-got-hacked-publicly-exposed-werkzeug-debugger/

(debug-enabled-python)

9 changes: 9 additions & 0 deletions new-test/req.txt
Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
Flask==2.0.1 # Intentionally older version
flask-sqlalchemy==2.5.1
PyJWT==1.7.1 # Vulnerable version
requests==2.25.0 # Older version with known vulnerabilities
python-dotenv==0.19.0
bcrypt==3.2.0
redis==3.5.3
pandas==1.3.0 # Older version
numpy==1.19.5 # Older version
Comment on lines +1 to +9
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💡 Verification agent

🧩 Analysis chain

Pinned older and vulnerable package versions.

Several packages here are intentionally pinned to older versions (e.g., Flask==2.0.1, PyJWT==1.7.1, requests==2.25.0). These are known to have security or stability issues. It's strongly recommended to upgrade to newer, patched versions to prevent potential security breaches or incompatibilities.

Below is a script you can run to check for any active security advisories on each pinned package:


🏁 Script executed:

#!/bin/bash
# Description: Check for known security advisories for pinned packages using GitHub GraphQL API.

packages=("Flask" "PyJWT" "requests" "python-dotenv" "bcrypt" "redis" "pandas" "numpy")

for pkg in "${packages[@]}"; do
  echo "Checking vulnerabilities for package: $pkg"
  gh api graphql -f query="query(\$name: String!) {
    securityVulnerabilities(first: 10, ecosystem: PIP, package: \$name) {
      nodes {
        advisory {
          summary
          publishedAt
          severity
        }
        vulnerableVersionRange
        firstPatchedVersion {
          identifier
        }
      }
    }
  }" -F name="$pkg"
  echo ""
done

Length of output: 10164


Action Required: Upgrade Vulnerable Package Versions

The current versions pinned in new-test/req.txt remain outdated and expose the project to known security vulnerabilities. Based on the GitHub GraphQL API checks:

  • Flask==2.0.1

    • Detected multiple high-severity issues (e.g., DoS and session cookie disclosure). Upgrading to versions ≥2.2.5 or ≥2.3.2 is recommended.
  • PyJWT==1.7.1

    • Falls within the vulnerable range (≥1.5.0, <2.4.0) due to key confusion attacks. An upgrade to at least 2.4.0 is advisable.
  • requests==2.25.0

    • Identified with several moderate to high vulnerabilities (e.g., session fixation and information leakage). Upgrading to version 2.32.0 or later is recommended.
  • numpy==1.19.5

    • Matches vulnerable version ranges concerning buffer overflow and other issues. Upgrading to ≥1.22 is recommended.

For the other packages listed (python-dotenv==0.19.0, bcrypt==3.2.0, redis==3.5.3, pandas==1.3.0), no active vulnerabilities were detected. However, the significant security concerns identified with Flask, PyJWT, requests, and numpy necessitate immediate action.

Action Items:

  • Upgrade Flask to a secure version (≥2.2.5 or ≥2.3.2 as per respective advisories).
  • Upgrade PyJWT to ≥2.4.0.
  • Upgrade requests to ≥2.32.0.
  • Upgrade numpy to at least 1.22.