Treating AI-generated code like human-written code in review is the wrong starting assumption. The failure modes are different. They repeat in predictable patterns. A normal PR review is not calibrated to catch them.
In March 2025, a post-incident report circulated among security teams at several mid-size SaaS companies describing a class of vulnerability that kept appearing in AI-assisted codebases: over-permissioned database queries generated by LLMs that had been trained on tutorial-grade examples. The queries worked. They passed tests. They passed review. They also handed authenticated users read access to rows they had no business seeing, because the model defaulted to the simplest join that satisfied the test case rather than the most restrictive one that satisfied the security requirement. Nobody was looking for that specific failure mode, because it did not match any pattern from the traditional secure code review checklist.
That incident illustrates a broader problem. The tooling and habits we use to review code were built for a world where a human author made intentional choices about what to include. AI-generated code is filled with choices the author did not make intentionally. The model made them statistically. That is a fundamentally different risk surface.
Why the Standard Checklist Leaves Gaps
A typical secure code review checks for the obvious categories: injection vulnerabilities, improper authentication, hardcoded secrets, unvalidated inputs, insecure deserialization. These are important. They are also not where AI-generated code tends to fail.
LLMs are trained on massive corpora of public code. A large fraction of that code is tutorial content, Stack Overflow answers, and hobbyist projects. These sources systematically underrepresent security requirements and overrepresent convenience. When a model generates a file upload handler, it produces code that handles the happy path correctly and omits the part where you validate MIME type against actual file contents rather than the extension, because the training examples almost never included that step. The model can generate secure code. It just defaults to the statistical center of the distribution, and the center of public code is not security-hardened.
The second gap is context blindness. A human engineer writing an internal admin API knows it is internal and applies a different set of defaults. An LLM generating that same API from a brief prompt does not have that context unless it was explicitly provided, and even when it is provided, the model's output often fails to reflect it consistently across a multi-file diff. The authentication middleware gets applied to the public routes. The admin routes, generated in a follow-up prompt, get it applied inconsistently or not at all.
The third gap is confidence without correctness. Human authors who are unsure about a security boundary tend to leave comments, ask questions in the PR, or reach for an existing utility. LLMs produce authoritative-looking code regardless of uncertainty. There is no signal in the output that the model was working outside its competence.
The Specific Checks a Dedicated AI Review Pass Needs
The review pass for AI-assisted changes needs a different checklist. Not a replacement for the standard one. An addition.
Authorization at the data layer, not just the route layer. This is the failure mode from the incident above. Review every database query in the diff and ask whether it is constrained by the authenticated user's identity. LLMs overwhelmingly generate queries scoped to a resource ID without verifying that the authenticated user owns that resource. A query like SELECT * FROM orders WHERE id = $1 will appear in AI output constantly. The correct version also filters by user_id. The model knows this pattern exists, but it does not default to it unless the surrounding context makes the requirement explicit.
Default-open network and permission configs. When an LLM generates infrastructure-adjacent code, IAM policies, Kubernetes manifests, S3 bucket configurations, it defaults toward permissive settings that make the demo work. In production these become blast radius amplifiers. Review every generated permission configuration with the assumption that it is over-permissioned until proven otherwise. This is the opposite of the usual review posture, where you assume the author applied least privilege unless you see an obvious violation.
Dependency selection rationale. AI-generated code introduces dependencies based on training data frequency. The model reaches for whatever package appeared most often in similar contexts in its training corpus. That is not a security evaluation. It is popularity weighting over a snapshot of the internet from two years ago. For every new dependency introduced in an AI-assisted diff, verify that the package is actively maintained, check its CVE history, and confirm it did not have a supply chain compromise in the last eighteen months. The rate at which AI code introduces new dependencies makes this a higher-frequency check than it would be in a human-authored PR.
Symmetric input validation. LLMs do well at generating validation logic for the input fields mentioned in the prompt. They do poorly at generating validation for implicit inputs: HTTP headers, query parameters appended to an endpoint, secondary inputs that arrive through a dependency's callback. Read the generated code as if you are an attacker, not a collaborator. What inputs reach logic that was not explicitly covered in the prompt?
Secrets handling in generated scaffolding. This one still matters even though it is on the standard checklist, because AI-generated scaffolding fails it at a higher rate than human-written code. The model will hardcode an API key in a config file if the example it was prompted with contained one. More subtly, it will generate .env.example files that contain realistic-looking fake secrets and real secret names, which then get copied into .env and committed. Grep for the pattern, not just for known secret formats.
One more check that gets missed almost universally: error message verbosity. LLMs generate expressive error messages because expressive error messages appear in tutorials and make debugging easier. In production, those messages leak stack traces, internal service names, database schema details, and occasionally user data. Every error path in an AI-assisted diff deserves a read specifically for information disclosure.
How to Build the Review Habit Without Slowing Down Shipping
The objection to adding a dedicated review pass is always velocity. If this takes an extra two hours per PR, teams will skip it. That is a real constraint, and ignoring it produces a checklist that nobody uses.
Scope the dedicated pass narrowly. Not every file in a PR needs the full AI-specific review. Files that contain authorization logic, database queries, file handling, external API calls, and configuration do. Files that contain pure business logic transformations, UI components with no data access, and test fixtures do not, or need it much less. A diff triage step that takes five minutes to identify which files get the deep pass makes the overall process fast enough to be sustainable.
Automate what can be automated. Static analysis tools like Semgrep can catch the over-permissioned query pattern if you write rules for it. Secret scanning is already a commodity. What cannot be automated yet is the context judgment: does this permission config match the intended exposure of this service? That part requires a human who understands the system's architecture.
OpenThunder approaches this problem by running AI-assisted changes through an architecture-aware verification layer that checks intent alignment, not just syntax. The gap between "the code does what the prompt asked" and "the code does what the system requires" is exactly where AI security failures hide, and closing that gap at review time is cheaper than closing it after a breach.
Teams using GitHub Copilot, Cursor, or any of the larger agentic coding tools should set an explicit policy: AI-assisted PRs get the additional review pass before merge, not as a cultural suggestion but as a merge requirement. The tooling to enforce this exists. The will to use it is the variable.
The deeper habit change is in how engineers brief the model. A prompt that includes authorization requirements, data ownership rules, and exposure context produces materially more secure output than a prompt that describes only functional behavior. That is not a security review technique. It is a generation technique. But it reduces the review burden by shifting some of the security work upstream to where it is cheapest to fix. It does not eliminate the review pass. It makes the review pass faster.
If your team has shipped more than a few thousand lines of AI-generated code without running a dedicated security pass calibrated to these failure modes, the question is not whether you have these vulnerabilities. The question is how many and where.
OpenThunder independently verifies AI-assisted changes against your architecture, security, and intent before they ship. Try it here.
See what OpenThunder verifies
Most teams know they need to review AI-generated code more carefully but reach for the same checklist they have always used. OpenThunder was built specifically for the gap between what standard review catches and what AI-generated code actually produces. See how it works at openthunder.ai.
The security debt accumulating in AI-assisted codebases is not a future problem. It is already there, and it is waiting for a checklist that knows where to look.