What are common mistakes in data sanitization?

What are common mistakes in data sanitization?

Common mistakes include not validating input, using outdated libraries, and failing to escape output.

Can you explain why input validation is important?

Input validation is crucial as it prevents malicious data from being processed, reducing security vulnerabilities.

What are some best practices for sanitizing data?

Best practices include using prepared statements, employing whitelists for input, and regularly updating your sanitization libraries.

How can I test if my sanitization is effective?

You can test by attempting to input various types of malicious data and checking if your application handles them correctly.