Exercise 37: Cloud Function Vulnerability via Insecure Input Handling

Objective:

Understand how insecure input handling in cloud functions, such as AWS Lambda or Google Cloud Functions, can lead to security vulnerabilities like code injection, SQL injection, or denial of service (DoS). Simulate attacks on insecure input handling and recommend strategies to secure cloud functions.

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Scenario:

An organization deploys a cloud function to process user input, such as query parameters or form data. The function lacks proper input validation and sanitization, making it vulnerable to injection attacks. An attacker exploits these vulnerabilities to execute malicious code, perform unauthorized actions, or crash the function. Your goal is to simulate this scenario, identify vulnerabilities, and provide mitigation techniques.

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Lab Setup:

Prerequisites:

1. Access to a cloud platform:
   • AWS Lambda or Google Cloud Functions.
2. Installed tools:
   • Burp Suite (Download).
   • SQLMap (Installation Guide).
   • aws-cli or gcloud CLI.

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Steps to Set Up the Lab:

1. Deploy a Vulnerable Cloud Function:
   • AWS Lambda:
     1. Create a Lambda function in Python that accepts user input and performs a database query without validation:pythonCopyEditimport json import sqlite3 def lambda_handler(event, context): user_input = event['queryStringParameters']['input'] conn = sqlite3.connect('/tmp/test.db') cursor = conn.cursor() # Insecure input handling query = f"SELECT * FROM users WHERE name = '{user_input}'" cursor.execute(query) result = cursor.fetchall() return { 'statusCode': 200, 'body': json.dumps(result) }
     2. Deploy the function and expose it using an API Gateway trigger.
   • Google Cloud Functions:
     1. Create a Cloud Function using Python with similar logic:pythonCopyEditimport json import sqlite3 from flask import Flask, request app = Flask(__name__) @app.route("/", methods=["GET"]) def handle_request(): user_input = request.args.get('input') conn = sqlite3.connect('/tmp/test.db') cursor = conn.cursor() # Insecure input handling query = f"SELECT * FROM users WHERE name = '{user_input}'" cursor.execute(query) result = cursor.fetchall() return json.dumps(result) if __name__ == "__main__": app.run(host="0.0.0.0", port=8080)
2. Initialize a Sample Database:
   • Prepopulate the database with sample user data:sqlCopyEditCREATE TABLE users (id INT, name TEXT, email TEXT); INSERT INTO users (id, name, email) VALUES (1, 'Alice', '[email protected]'), (2, 'Bob', '[email protected]');
3. Deploy the Cloud Function:
   • AWS:
     • Use API Gateway to deploy the Lambda function publicly.
   • Google Cloud:
     • Deploy the function with public access using:bashCopyEditgcloud functions deploy vulnerable-function \ --runtime python39 \ --trigger-http \ --allow-unauthenticated

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Exercise: Exploiting Insecure Input Handling

Objective:

Simulate attacks by injecting malicious input into the cloud function.

1. Test Input Validation:
   • Use a browser or a tool like Postman to send a query:vbnetCopyEdithttp://<function-url>?input=Alice
   • Verify that the function processes valid input.
2. Perform SQL Injection:
   • Inject malicious input to extract all users from the database:perlCopyEdithttp://<function-url>?input=' OR '1'='1
   • Use Burp Suite to automate SQL injection testing by modifying requests.
3. Test for Command Injection (Optional):
   • Modify the function to execute OS commands (e.g., using os.system()):pythonCopyEditresult = os.system(user_input)
   • Send input like:bashCopyEdit; ls /
4. Simulate a Denial of Service Attack:
   • Send a large payload to crash the function:vbnetCopyEdithttp://<function-url>?input=<10000-character-string>
5. Analyze Logs:
   • Check cloud function logs for evidence of malicious input or crashes:
     • AWS CloudWatch Logs:bashCopyEditaws logs tail /aws/lambda/<function-name>
     • Google Cloud Logs:bashCopyEditgcloud functions logs read vulnerable-function

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Tools Required:

1. AWS Lambda or Google Cloud Functions: For deploying the vulnerable cloud function.
2. Burp Suite: For testing input validation and injection attacks.
3. SQLMap: For automating SQL injection testing.

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Deliverables:

1. Exploit Report:
   • Evidence of successful SQL injection, command injection, or DoS attacks.
   • Logs or screenshots showing the impact of malicious input on the cloud function.
2. Recommendations:
   • Best practices for securing cloud functions against input handling vulnerabilities.

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Solution:

1. Identified Vulnerabilities:
   • No Input Validation: User input was directly used in database queries.
   • No Output Encoding: Function responses exposed raw database results.
   • No Rate Limiting: Excessive requests caused denial of service.
2. Consequences:
   • Data Breach: Sensitive user data was exposed through SQL injection.
   • Function Downtime: Malicious payloads caused function crashes or performance issues.
   • System Compromise: Command injection allowed unauthorized OS command execution.
3. Prevention Techniques:
   • Input Validation:
     • Validate and sanitize all user inputs using libraries like validators or bleach:pythonCopyEditif not user_input.isalnum(): return {'statusCode': 400, 'body': 'Invalid input'}
   • Parameterized Queries:
     • Use parameterized queries to prevent SQL injection:pythonCopyEditcursor.execute("SELECT * FROM users WHERE name = ?", (user_input,))
   • Apply Rate Limiting:
     • Use API Gateway (AWS) or Cloud Armor (Google) to restrict the number of requests.
   • Enable Logging and Alerts:
     • Log all function activity and set alerts for anomalous behavior.
   • Implement Exception Handling:
     • Ensure the function gracefully handles errors and logs details without exposing sensitive information.

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Conclusion:

This exercise demonstrates how insecure input handling in cloud functions can lead to significant vulnerabilities like SQL injection, command injection, or denial of service. By implementing input validation, parameterized queries, and logging, organizations can secure cloud functions and mitigate these risks.