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Step 1: Generate a Simple Application Using AI
Begin by asking an AI coding assistant to create a basic web application, such as a user registration and login system or a task management application with authenticated users.
For example, use the following prompt:
"Create a simple web application with user registration, login, and a dashboard. Include input validation, authentication, database integration, and secure session management. Follow secure coding best practices."
Once the AI generates the application, review its architecture, source code, configuration files, and dependencies. Do not assume that mentioning security in the prompt guarantees a secure result. The objective is to evaluate the actual implementation rather than the AI's claims about its quality.
Step 2: Check for Insecure Dependencies
Applications often depend on third-party libraries and frameworks. Although these dependencies can simplify development, outdated or vulnerable packages may introduce security risks.
Inspect the project's dependency files and use appropriate tools to identify known vulnerabilities. Depending on the technology stack, tools such as npm audit, pip-audit, or OWASP Dependency-Check can help identify vulnerable components.
For example, an AI-generated application might specify an outdated package version with a publicly documented vulnerability. If that package is used in an exposed part of the application, attackers could potentially exploit the weakness.
How to fix it: Update vulnerable dependencies to supported versions, remove unnecessary packages, and review security advisories before deployment. Use a lockfile to improve dependency consistency, and run dependency scans regularly.
Remember that automated scanners cannot identify every risk. A package without known vulnerabilities may still be unsuitable for a particular application or contain weaknesses that have not yet been publicly reported.
Step 3: Identify Injection Vulnerabilities
Injection attacks occur when an application treats untrusted input as executable instructions. SQL injection is a common example, particularly in applications that interact with databases.
Suppose an AI-generated login function constructs a database query by combining a username directly with a SQL statement. If user input is inserted into the query without proper handling, an attacker may manipulate the query's intended behavior.
For example, vulnerable code might build a query using string concatenation rather than parameterized database operations. This creates an opportunity for malicious input to interfere with database commands.
How to fix it: Use parameterized queries or prepared statements, validate input according to the application's requirements, and apply context-appropriate output encoding. Avoid relying exclusively on input filtering because filtering alone may not prevent injection attacks.
Security testing should also examine cross-site scripting (XSS), command injection, and other injection risks where relevant. For web applications, test both the server-side processing of user input and how returned data is displayed in the browser.
Step 4: Test Authentication and Authorization
Authentication determines who a user is, while authorization determines what that user is permitted to do. AI-generated applications may implement login functionality without adequately enforcing access restrictions.
For example, a task management application might require users to sign in before opening a dashboard but fail to verify that each requested task belongs to the authenticated user. An attacker could potentially change a task identifier in a request to access another user's information.
This type of weakness can expose private records even when the application has a working login system.
How to fix it: Enforce authorization checks on the server for every sensitive operation. Verify that the authenticated user has permission to access or modify the requested resource. Never rely solely on hidden interface elements or client-side checks to protect sensitive functionality.
Test access controls using accounts with different permissions. Confirm that unauthenticated users cannot access protected endpoints and that ordinary users cannot perform administrative actions or access another user's records.
Step 5: Search for Hardcoded Secrets
Hardcoded secrets are another common risk in AI-generated code. An application may include database passwords, API keys, access tokens, or cryptographic keys directly in its source files.
For example, the generated code might contain a database connection string with a username and password embedded in it. If the code is committed to a public repository or shared with an unauthorized party, those credentials could be exposed.
How to fix it: Store sensitive configuration values in environment variables or an appropriate secrets management system. Ensure that secret files are excluded from version control and use secret-scanning tools to detect accidental exposure.
If a real credential has already been exposed, removing it from the source code is not enough. Revoke or rotate the compromised credential and investigate whether it has been misused.
Step 6: Compare the Original Code with the Corrected Version
After identifying vulnerabilities, document each finding, assess its potential impact, and implement a correction.
For example, the original implementation may use dynamically constructed SQL queries, while the corrected version uses parameterized queries. Similarly, an insecure endpoint may be updated to enforce server-side authorization, and embedded credentials may be replaced with securely managed configuration values.
Repeat the security tests after making these changes. Verify that the corrected code prevents the original attack scenario without breaking legitimate application functionality.
A useful comparison should record the vulnerability, its potential consequences, the remediation applied, and the test results. This creates an auditable record of the improvements made during the review.
Step 7: Combine AI Assistance with Security Testing
AI can also assist with remediation by explaining suspicious code, suggesting safer alternatives, and helping developers write security tests. However, AI-generated fixes require the same scrutiny as AI-generated application code.
Combine manual code reviews with static application security testing (SAST), dynamic application security testing (DAST), dependency scanning, and appropriate penetration testing. Where relevant, use the OWASP Top 10 as a reference for common web application security risks.
No single tool can identify every vulnerability. A layered testing approach provides stronger assurance than relying exclusively on an AI assistant or an automated scanner.
Begin by asking an AI coding assistant to create a basic web application, such as a user registration and login system or a task management application with authenticated users.
For example, use the following prompt:
"Create a simple web application with user registration, login, and a dashboard. Include input validation, authentication, database integration, and secure session management. Follow secure coding best practices."
Once the AI generates the application, review its architecture, source code, configuration files, and dependencies. Do not assume that mentioning security in the prompt guarantees a secure result. The objective is to evaluate the actual implementation rather than the AI's claims about its quality.
Step 2: Check for Insecure Dependencies
Applications often depend on third-party libraries and frameworks. Although these dependencies can simplify development, outdated or vulnerable packages may introduce security risks.
Inspect the project's dependency files and use appropriate tools to identify known vulnerabilities. Depending on the technology stack, tools such as npm audit, pip-audit, or OWASP Dependency-Check can help identify vulnerable components.
For example, an AI-generated application might specify an outdated package version with a publicly documented vulnerability. If that package is used in an exposed part of the application, attackers could potentially exploit the weakness.
How to fix it: Update vulnerable dependencies to supported versions, remove unnecessary packages, and review security advisories before deployment. Use a lockfile to improve dependency consistency, and run dependency scans regularly.
Remember that automated scanners cannot identify every risk. A package without known vulnerabilities may still be unsuitable for a particular application or contain weaknesses that have not yet been publicly reported.
Step 3: Identify Injection Vulnerabilities
Injection attacks occur when an application treats untrusted input as executable instructions. SQL injection is a common example, particularly in applications that interact with databases.
Suppose an AI-generated login function constructs a database query by combining a username directly with a SQL statement. If user input is inserted into the query without proper handling, an attacker may manipulate the query's intended behavior.
For example, vulnerable code might build a query using string concatenation rather than parameterized database operations. This creates an opportunity for malicious input to interfere with database commands.
How to fix it: Use parameterized queries or prepared statements, validate input according to the application's requirements, and apply context-appropriate output encoding. Avoid relying exclusively on input filtering because filtering alone may not prevent injection attacks.
Security testing should also examine cross-site scripting (XSS), command injection, and other injection risks where relevant. For web applications, test both the server-side processing of user input and how returned data is displayed in the browser.
Step 4: Test Authentication and Authorization
Authentication determines who a user is, while authorization determines what that user is permitted to do. AI-generated applications may implement login functionality without adequately enforcing access restrictions.
For example, a task management application might require users to sign in before opening a dashboard but fail to verify that each requested task belongs to the authenticated user. An attacker could potentially change a task identifier in a request to access another user's information.
This type of weakness can expose private records even when the application has a working login system.
How to fix it: Enforce authorization checks on the server for every sensitive operation. Verify that the authenticated user has permission to access or modify the requested resource. Never rely solely on hidden interface elements or client-side checks to protect sensitive functionality.
Test access controls using accounts with different permissions. Confirm that unauthenticated users cannot access protected endpoints and that ordinary users cannot perform administrative actions or access another user's records.
Step 5: Search for Hardcoded Secrets
Hardcoded secrets are another common risk in AI-generated code. An application may include database passwords, API keys, access tokens, or cryptographic keys directly in its source files.
For example, the generated code might contain a database connection string with a username and password embedded in it. If the code is committed to a public repository or shared with an unauthorized party, those credentials could be exposed.
How to fix it: Store sensitive configuration values in environment variables or an appropriate secrets management system. Ensure that secret files are excluded from version control and use secret-scanning tools to detect accidental exposure.
If a real credential has already been exposed, removing it from the source code is not enough. Revoke or rotate the compromised credential and investigate whether it has been misused.
Step 6: Compare the Original Code with the Corrected Version
After identifying vulnerabilities, document each finding, assess its potential impact, and implement a correction.
For example, the original implementation may use dynamically constructed SQL queries, while the corrected version uses parameterized queries. Similarly, an insecure endpoint may be updated to enforce server-side authorization, and embedded credentials may be replaced with securely managed configuration values.
Repeat the security tests after making these changes. Verify that the corrected code prevents the original attack scenario without breaking legitimate application functionality.
A useful comparison should record the vulnerability, its potential consequences, the remediation applied, and the test results. This creates an auditable record of the improvements made during the review.
Step 7: Combine AI Assistance with Security Testing
AI can also assist with remediation by explaining suspicious code, suggesting safer alternatives, and helping developers write security tests. However, AI-generated fixes require the same scrutiny as AI-generated application code.
Combine manual code reviews with static application security testing (SAST), dynamic application security testing (DAST), dependency scanning, and appropriate penetration testing. Where relevant, use the OWASP Top 10 as a reference for common web application security risks.
No single tool can identify every vulnerability. A layered testing approach provides stronger assurance than relying exclusively on an AI assistant or an automated scanner.