AI Code Security: Catching Vulnerabilities AI Agents Introduce

AI coding agents can introduce security vulnerabilities the same way any developer can, but the specific patterns tend to differ somewhat from typical human-introduced vulnerabilities, which matters for how security review should actually be structured when a meaningful share of code is AI-generated.

Plausible-looking but insecure patterns

AI models generate code based on patterns learned from a huge volume of training data, which includes both secure and insecure real-world code. A model can generate code that looks conventional and reasonable, using string concatenation to build a database query instead of parameterized queries, for instance, because that pattern appears often enough in training data to seem normal, even though it's a well-known vulnerability pattern that security-conscious code deliberately avoids. This is a genuinely tricky category because the generated code often looks unremarkable rather than obviously wrong, which means it can pass a casual read-through review that isn't specifically looking for known vulnerability patterns.

Missing input validation on edge cases

AI-generated code handling user input sometimes covers the expected, well-formed input case correctly while not adequately validating unexpected or malicious input, a pattern related to the general edge-case weakness of AI-generated code, but with specific security implications when the edge case in question is a deliberately malicious input rather than just an unusual valid one. Explicitly reviewing any AI-generated code that processes external or user-supplied input for validation completeness, not just functional correctness, is a necessary specific check, since "the code works for normal input" and "the code is safe against adversarial input" are genuinely different bars.

Dependency and library choices without security vetting

An AI agent selecting a library or package to accomplish a task is generally choosing based on functional fit for the described task, not on an assessment of that dependency's security track record, maintenance status, or known vulnerabilities. Reviewing any new dependency an AI agent introduces with the same scrutiny you'd apply to a human developer's dependency choice, checking for known vulnerabilities, maintenance activity, and whether it's actually a reasonable, trustworthy choice for the specific use case, is a necessary step that doesn't happen automatically just because the code that uses the dependency looks correct.

Secrets and credentials handling

AI agents working across a codebase can sometimes generate code that logs sensitive information, includes a hardcoded example credential that looks like a placeholder but resembles a real pattern, or handles secrets in a way that's functionally correct but not actually secure practice, storing something in plain text that should be encrypted, for instance. Specifically checking any AI-generated code that touches credentials, API keys, or sensitive data for secure handling practices, not just functional correctness, catches this category before it becomes a real exposure.

Building security review into the agent workflow itself

Rather than treating security review as a separate, later step after AI-generated code is otherwise accepted, integrating automated security scanning tools directly into the review process for AI-generated changes, the same static analysis and vulnerability scanning tools used for human-written code, catches a meaningful share of these patterns automatically before they require a security-specialist's manual review time for every single change.

How to actually approach this

1. Specifically check AI-generated code for known insecure patterns, since these can look conventional rather than obviously wrong.
2. Review input validation completeness for adversarial input, not just functional correctness for expected, well-formed input.
3. Vet any new dependency an AI agent introduces, since dependency selection isn't automatically security-conscious just because the resulting code works.
4. Run automated security scanning on AI-generated changes as a standard part of the review pipeline, not an occasional manual check.

Final thoughts

AI-generated code security risks follow recognizable patterns, plausible-looking insecure code, incomplete adversarial input validation, unvetted dependencies, secrets handled functionally but not securely, that are worth specifically checking for rather than assuming general code review will automatically catch. Teams that adjust their security review process to specifically account for these AI-generated patterns, including automated scanning integrated into the workflow, catch meaningfully more real vulnerabilities than teams applying the same review process they used before AI-generated code became a significant share of their codebase.