How to Catch Security Bugs in AI-Generated Code

What Makes AI-Generated Code Vulnerable

 

These are the most common issues we see when auditing AI-generated code:

 

1. Missing input validation

 

Many LLMs return functions that assume inputs are correct. They skip basic checks for data types, encoding, or malicious content. This opens the door to injection attacks, data corruption, and unexpected behavior under normal user interaction. This is a red flag in secure AI-generated code reviews.

 

2. Insecure defaults

 

Generated code often includes permissive configurations — broad CORS policies, unrestricted file access, or open admin routes. These patterns may work in development, but create real exposure when shipped without review.

 

3. Weak authentication logic

 

When asked to write login flows, LLMs frequently mishandle password storage, token validation, or session expiration. These are high-risk areas that require precision. Trusting generated output here can result in serious LLM code vulnerabilities.

 

4. No error handling or logging

 

AI-generated functions tend to suppress errors or leave them unhandled. This not only hides failures but also makes intrusion detection and debugging more difficult. Without logging or alerts, teams lose visibility into how the system behaves under stress.

 

5. Reuse of insecure patterns

 

LLMs sometimes echo flawed code seen in training data (e.g outdated cryptography, broken authorization flows, or legacy APIs). Even experienced developers can miss these flaws if they assume the AI chose the “best practice.” Identifying these requires deep security testing for AI code beyond functional checks.

 

Why Traditional Review Often Misses These Gaps

 

Here’s why traditional reviews often miss critical issues in AI-generated code:

 

1. Too much trust in clean output

 

LLM-written code often looks polished. It uses modern syntax, clear naming, and passes basic tests. This lowers reviewer skepticism, even when the underlying logic is flawed.

 

2. Lack of threat modeling context

 

AI doesn’t understand user behavior, abuse cases, or business constraints. If reviewers assume the code aligns with the original intent, they miss how the AI may have taken shortcuts or introduced unintended behavior.

 

3. Volume of generated code

 

AI can write more code than a human would in the same timeframe. That means more lines to review, more logic to trace, and less time to evaluate each piece. Review becomes surface-level by necessity. Without extra effort, security testing for AI code just doesn’t happen.

 

4. Missing tests and coverage gaps

 

Most AI-generated code doesn’t include security-related tests or edge-case handling. If reviewers don’t write those tests manually (or sandbox the code), they rarely catch the weak spots.

 

Sandbox Strategies You Can Use to Catch AI-Created Bugs

 

Here are five sandbox testing strategies you can use to surface risk early and ship more secure AI-generated code:

 

1. Input fuzzing to expose weak assumptions

 

Feed malformed, extreme, or malicious inputs into AI-generated functions to observe failure patterns. This includes:

 

• Oversized strings
• Unexpected characters
• Type mismatches
• Deliberately invalid tokens or user roles

 

Fuzzing surfaces input validation gaps that wouldn’t show up in basic happy-path tests.

 

2. Static analysis with LLM-aware rules

 

Run tools like Semgrep or SonarQube with custom rule sets tuned for AI tendencies, such as:

 

• Unescaped input in SQL queries
• Missing CSRF protection
• Insecure JWT validation logic

 

These tools catch recurring LLM code vulnerabilities that developers might overlook in review.

 

3. Behavior testing in isolated environments

 

Sandbox critical flows and simulate real usage: logins, failed auth attempts, edge-case API calls. This helps reveal whether generated logic gracefully handles real-world behavior or collapses under stress.

 

4. Red-team style prompting

 

Prompt the same AI model to attack its own output.

 

Example: “Given this login function, write an input that bypasses validation.”

 

This approach often reveals logic flaws or poorly scoped conditionals that attackers could exploit.

 

5. Prompt-aware diffing

 

Compare generated code to known-safe implementations to catch subtle differences. Even small deviations from proven patterns can create risk. Prompt-aware diffing highlights what changed, why it changed, and what might go wrong as a result.