Security Testing for AI-Assisted Code: What Changes When Developers Use Copilots

AI coding assistants have been adopted rapidly by engineering teams because the productivity case is genuine: they reduce the time to write boilerplate, accelerate unfamiliar language tasks, and surface API patterns that developers would otherwise need to look up. What is less often discussed is the security profile of the code they generate. AI coding assistants produce syntactically correct code that can be functionally adequate while containing security vulnerabilities at rates that differ meaningfully from careful human-written code in specific categories.

This does not mean AI-assisted code is inherently less secure than human-written code. It means the vulnerability profile is different, and security testing processes that were calibrated for human-written code may not be optimally configured for a codebase where a significant proportion of the implementation was generated. Understanding what changes and adjusting accordingly is more useful than either dismissing the risk or avoiding AI assistance on security grounds.

How AI-Generated Code Fails Securely

The security failure modes of AI-generated code cluster in specific categories. Cryptographic implementation is a consistent weak point: AI assistants tend to suggest deprecated algorithms, incorrect parameter choices, and implementation patterns that are syntactically valid but cryptographically unsound. Input validation and output encoding are another cluster: generated code often handles the happy path correctly while omitting or incorrectly implementing validation for edge cases and adversarial inputs. SQL query construction is a third: despite the widespread availability of parameterised query patterns, AI assistants will occasionally generate string concatenation approaches that introduce injection risk, particularly when asked to handle complex dynamic queries.

The underlying reason for these failure modes is that AI coding assistants are trained on large corpora of code, which includes a significant proportion of code with security issues. The model has no reliable way to weight secure implementations more heavily than insecure ones unless that preference is explicitly reflected in the training signal. When generating code for a pattern that has both secure and insecure common implementations in the training data, the model may produce either. This is why review processes that check for specific vulnerability categories are more effective than general code review for AI-generated code.

What Changes in the Review Process

Code review processes for AI-assisted development should be calibrated toward the specific vulnerability categories where AI tools are known to underperform. This means checklists in code review that explicitly call out cryptographic choices, input validation completeness, query parameterisation, and authentication implementation for any AI-generated sections. Generic "check for security issues" prompts in a review template are less effective than specific checks tied to known failure patterns.

It also means that automated static analysis configuration should be reviewed and potentially tuned when AI assistance becomes a significant part of the development process. If your static analysis tool is configured to suppress certain rule categories because human developers rarely make those mistakes, those suppressions may need to be reconsidered for an AI-assisted codebase. The categories where AI tools fail more frequently may be exactly the categories that have been deprioritised in your automated tooling.

Testing Density and Coverage

One practical implication of AI-assisted development for security testing is the potential for higher code volume. AI tools increase development velocity, which means more code may be produced in a given period. If security testing cadence is fixed, more code is accumulating between tests. The coverage of each test relative to the total codebase may be decreasing even if the absolute amount of testing remains constant. This is worth tracking explicitly if your organisation has significantly increased development velocity through AI assistance.

Pen test scoping should account for the AI assistance model. If a significant portion of a feature was generated by an AI tool, that feature warrants slightly more attention on the vulnerability categories where AI tools are known to underperform, rather than the same generic approach applied to all features. This is not about testing AI-generated code more thoroughly in absolute terms, but about applying test effort to the categories where the risk is higher given how the code was written.

Training and Developer Awareness

Security training for developers who use AI coding assistants needs to cover how to evaluate the output they are accepting. The risk is not that developers will blindly accept all AI suggestions without any review. The risk is that they will apply the same review heuristics to AI-generated code that they apply to code they wrote themselves, which are heuristics calibrated to their own common mistakes rather than to the failure patterns of the model. Training should specifically cover: what security properties to check in AI-generated code, how to identify the categories of suggestion that are most likely to contain issues, and how to use the AI tool to generate more secure alternatives when an initial suggestion has issues.

This is an area where training from your own security testing results is particularly valuable. If your pen test or code review has identified AI-generated code with specific security issues, using those real examples in developer training is far more effective than abstract guidance. Developers who can see a real example of how an AI-generated cryptographic implementation failed, and understand why the model made that choice, are better equipped to catch similar issues in future than developers who have only been told to "be careful with AI-generated code."

• Identify the specific vulnerability categories where AI tools underperform and target review effort there
• Review and update static analysis configurations for an AI-assisted codebase
• Track whether code volume growth is outpacing testing coverage
• Adjust pen test scoping to reflect AI-assisted development patterns
• Train developers on AI-specific failure modes using real examples from your own testing history

We help engineering teams adapt their security testing and review processes for AI-assisted development. If this is relevant to your organisation, reach out at info@cyberlinx.com.au.