## 3 Materials and Methods
### Methodology for Database Establishment  
Figure 2 includes a representative example of SQL code used to create the database and the table, as well as to simulate the insertion of user data. The SQL script was generated by ChatGPT based on the following query:  
* _\"Generate a script to set up a database named'my_database' and a table named 'user_table' in MySQL. The user table should have fields for user ID, unique username, password hash, and salt. Also, include a sample insertion to add a user named'sampleUser' with an encrypted password and salt\"._  
Initially, the code checks if a database named \"my_database\" exists, creating it if not. Then, the code selects this database for subsequent operations. A table named \"users\" is created if it does not already exist, defining its structure to store user information, including a unique identifier (user_id), non-null and unique username, password hash, and salt value. A sample user is inserted into the \"users\" table with simulated values. A comment notes that the password has been hashed and salted in PHP, highlighting the importance of securely storing passwords. Additionally, the code provides commented PHP lines exemplifying how to verify a password. This code snippet demonstrates database setup, table creation, and password handling practices in a secure manner. Figure 2 highlights the syntax for better understanding, where a blue color is used to easily recognize the SQL  
Figure 1: Components of the working model for an industrial application. Figure 2: SQL code for creating a database, provided by ChatGPT. keywords (e.g., create, if not exists, or primary key). Additionally, comments in both SQL and PHP, denoted by -- in SQL and // in PHP, are displayed in a gray color, to clearly distinguish them from the executable code. Moreover, specific values, including database names (my_database), table names (users), column names (user_id, username, etc.) and data types (VARCHAR, INT, etc.) are presented in black or purple colors, for clarity and differentiation within the code structure. The code provided by ChatGPT in Figure 2 lacks certain security measures related to database management and password handling. It inserts a user with a plaintext password directly into the database, failing to employ proper encryption practices. While the code includes a comment on the hypothetical hashing and salting of the password, this is not reflected in the actual insertion of data. Furthermore, the absence of specific password complexity constraints, such as minimum length or the inclusion of special characters, is a notable security gap. The code also neglects the use of prepared statements, a recommended practice for preventing SQL injection attacks. Additionally, the handling of uniqueness constraints for the username is not explicitly addressed in the code. In a production environment, these security considerations are crucial for safeguarding data integrity and password security. The issue lies in the fact that LLMs do not inherently generate secure code unless explicitly specified. This stems from the training data, predominantly consisting of programs created prior to considering potential security vulnerabilities such as SQL injections. The models tend to replicate the patterns present in the training data and, since secure coding practices were not comprehensively integrated at the time of training, LLMs may inadvertently generate code that lacks robust security measures. Consequently, the responsibility falls on developers to consciously incorporate secure coding practices and address potential vulnerabilities in the code generated by LLMs, to ensure a heightened level of security. To obtain secure code that establishes a MySQL database, the user should have specified the following sentence at the prompt:  
* _\"Generate a secure script to set up a MySQL database named'my_database' and create a table named 'user_table'. The user table should include fields for user ID, unique username, password hash, and salt.