AI code checker: how to validate AI-generated code before it ships

An AI code checker inspects source code for defects and outputs a list of issues, and the better ones also propose or submit corrections. It reads code without running it, flags problems like injection flaws or insecure data handling, and reports where each issue lives and why it matters. The label describes a function, not a specific product.

The name misleads on one point. An AI code checker examines code regardless of who or what wrote it. A tool that reviews a pull request applies the same analysis whether a developer typed the change or an assistant like GitHub Copilot generated it. AI-written code earns extra attention because it arrives in higher volume, but the check itself treats all code the same way.

Most tools in this category split into two groups by what they produce. The first group scans and flags. It surfaces vulnerabilities, ranks them, and hands the developer a report to act on. Snyk Code, a static analysis product that catches security issues as code is written, fits here and does that job well (Contrary Research).

The second group reviews and fixes. It finds the issue, then writes a correction and submits it for a human to approve. Hyrax works this way. It reviews all code continuously, finds problems across security, correctness, maintainability, performance, architecture, and operations, and submits pre-verified fixes as pull requests. A person always approves before anything merges. The distinction between flagging and fixing shapes how much work reaches the developer, and the rest of this page returns to it.

Why AI-generated code still needs review#

AI-generated code carries the same security flaws as human-written code, just faster and in greater volume. Veracode tested over 100 large language models across 80 coding tasks and found that 45% of AI-generated code contains security flaws. Only 55% passed as secure. Endor Labs cites academic studies reaching the same conclusion, with over 40% of AI-generated solutions containing flaws even in the latest models.

The flaws cluster in familiar categories. Veracode's four-CWE study found models generated insecure cross-site scripting code 86% of the time and insecure log injection code 88% of the time, largely from poor data sanitization. SQL injection and cryptographic failures fared better, with 80% and 86% pass rates, but a fifth of database queries and a seventh of crypto implementations still shipped vulnerable. These are the same injection, XSS, and crypto weaknesses that reviewers already look for in code a person wrote.

The rate varies sharply by language. Python passed 62% of the time and JavaScript 57%, while Java passed only 29%. Veracode attributes the Java gap to training data predating modern security practices, since the language has a long history as a server-side platform with many insecure examples in circulation.

The cause explains why bigger models do not fix the problem. Veracode found security performance largely unchanged across model generations, even as syntactic correctness improved. Models learn from repositories containing both secure and insecure code and inherit the insecure patterns. They also lack security context, so they do not know the business logic or architecture a given function protects. A model cannot perform the interprocedural dataflow analysis needed to trace which variables reach a database call and require sanitization, so it guesses.

Endor Labs adds risks specific to AI-written code. A simple to-do app prompt generated two to five backend dependencies depending on the model. Models also suggest packages that do not exist, and attackers register those names with malicious code. Reviewing AI-generated code means checking the same flaw classes as always, plus a few new ones, at the speed the model produces them.

The six domains a real code check covers#

A code check scoped to security alone misses most of what breaks in production. Injection flaws and hardcoded secrets are real problems, but so are logic errors that pass every security scan, functions that grow untestable, and queries that degrade under load. Hyrax reviews code across six domains, and each catches a distinct class of defect that a security-only scanner reports as clean.

Security catches injection paths, unsafe deserialization, weak cryptography, exposed credentials, and missing authorization checks before they reach a build.

Correctness catches logic errors, off-by-one mistakes, null dereferences, and unhandled edge cases that produce wrong results without raising an alarm.

Maintainability catches duplicated logic, dead code, unclear naming, and functions that have grown too large to test or change safely.

Performance catches inefficient queries, unbounded loops, redundant computation, and memory patterns that hold up under review but fail under real traffic.

Architecture catches boundary violations, circular dependencies, and coupling that drifts from the intended structure as code changes hands.

Operations catches missing error handling, absent logging, poor observability, and configuration that behaves differently across environments.

Generic static analysis tools like Snyk Code do the security domain well. Snyk Code runs continuously inside IDEs, pull requests, and CI pipelines, using application context to prioritize findings and extend coverage to code influenced by AI-assisted tooling (Contrary Research). That is valuable work, and it stays inside one domain by design. A correctness bug, an architectural violation, or a performance regression sits outside what a security scanner is built to report.

The gap matters more with AI-written code because volume rises faster than review capacity. A generated function can be free of injection flaws and still return the wrong value, couple two modules that should stay separate, or issue a query that scans an entire table. Hyrax evaluates all six domains on every change, so a defect that clears security review still surfaces where it lives.

How Hyrax runs the check-and-fix loop#

Hyrax reviews all code in the repository, not the subset written by AI coding tools. It runs continuously as commits arrive, finds issues across the six domains, and submits fixes as pull requests that a person approves before anything merges. Nothing merges on its own.

The loop starts with a review pass over the codebase. Hyrax reads the code the way a reviewer would, tracing data flow, checking control paths, and comparing patterns against known failure classes in security, correctness, maintainability, performance, architecture, and operations. When it finds an issue, it does not stop at a flag. It writes the fix.

A fix reaches a pull request only after Hyrax verifies it. Pre-verified means Hyrax has applied the change against the actual codebase, run the existing tests, and confirmed the fix resolves the issue without breaking the build or altering unrelated behavior. A change that fails verification does not become a pull request. The reviewer sees proposed fixes that already compile and pass, not raw suggestions that need debugging before they can be judged.

Every fix arrives as a standard pull request with a description of the problem, the change, and the verification result. A person reads it, decides, and merges or rejects. Hyrax has no merge permission. That approval step stays with the engineer who owns the code, which keeps the review and fixing under human control while removing the manual work of writing each fix by hand.

The loop closes when the pull request merges, and it starts again on the next change. Hyrax is not a one-time scan that produces a report and goes quiet. It runs against new commits as they arrive in the repository, so the review-and-fix cycle tracks the codebase as it grows rather than snapshotting it once. Issues that appear in a Tuesday commit surface as verified pull requests without waiting for a scheduled audit.

Continuous review changes what an engineer spends time on. Instead of triaging a backlog of findings and then writing fixes for the ones that survive, the engineer reviews finished, tested changes and approves the ones that belong. Hyrax finds the issue, fixes it, verifies it, and ships it to a pull request. The engineer closes the loop by merging.

AI code checkers and platforms like Snyk#

Snyk Code and Hyrax solve different halves of the same problem, and a team running both gets more than either alone. Snyk Code is a static application security testing product that identifies vulnerabilities in source code as it is written, catching injection flaws, insecure data handling, and logic errors before build or deployment (Contrary Research). Its analysis runs continuously inside editors, pull requests, and CI pipelines, and it covers modern inputs including code produced by AI-assisted tooling. That security depth is established and worth keeping.

Where Snyk Code concentrates on finding and flagging security vulnerabilities, Hyrax reviews all code across six domains and submits the corrections as pull requests. Security is one of those domains. Correctness, maintainability, performance, architecture, and operations sit alongside it, so a bug that compiles cleanly and passes a security scan still gets caught. A fixing layer extends the workflow rather than duplicating the scan beneath it.

The two fit together in sequence. Snyk Code surfaces the security findings a scanner is built to surface. Hyrax reviews the same codebase for the broader classes of issues, writes verified fixes, and submits them for a human to approve. Neither replaces the other, and a team keeping Snyk Code for security while adding Hyrax for continuous review and fixing closes more issues than a security scan alone.

What to check for before shipping AI-generated code#

Run each of these checks against AI-generated code before it merges. The vulnerability data shows where the failures cluster, so start with the highest-risk classes.

Input validation and injection. Trace every value that reaches a query, a shell command, or a rendered page. Missing input sanitization ranks as the most common flaw in LLM-generated code, spanning SQL injection, OS command injection, and cross-site scripting, which models fail to write securely 86% of the time. Confirm parameterized queries and output encoding rather than string concatenation.

Hardcoded credentials and auth gaps. Search generated code for embedded API keys, passwords, and tokens. Prompts that skip explicit security guidance often produce broken authentication, broken access control, and hard-coded credentials. Verify that authentication guards every protected route and that access checks match the intended permission model.

Cryptographic and logging handling. Check that hashing, encryption, and random-value generation use current algorithms, since models produce insecure crypto implementations 14% of the time. Review log statements for unsanitized user input, the source of log injection flaws that appear in 88% of generated cases.

Hallucinated and excess dependencies. Confirm every imported package exists and comes from a legitimate source. Models invent non-existent packages that attackers can register with malicious code, and a simple prompt can pull in several backend dependencies the task never required. Pin versions and audit the full dependency graph.

Architectural drift. Compare the generated design against the existing security invariants of the codebase. Subtle design changes can break those invariants while evading static analysis and manual review, so check that trust boundaries, data flows, and separation of concerns still hold.

FAQ#

Does Hyrax only check AI-written code?

No. Hyrax reviews all code in the repository, regardless of whether a person or an AI coding tool wrote it. AI-generated code carries the same vulnerability classes as human code, so a checker scoped to one author type misses issues in the other. Reviewing every commit means Hyrax finds and fixes problems across the whole codebase.

How does Hyrax pricing work?

Hyrax runs on credits, with a Free plan and a Paid plan. The Free plan lets a team run code review and fixing at no cost, and the Paid plan adds more credits for larger codebases and higher review volume. Credits track the review and fixing work Hyrax performs, so a team pays for what it uses.

Does Hyrax auto-merge fixes?

No. Hyrax submits every fix as a pull request that a human reviews and approves. Nothing merges without that approval. Each fix is verified before it reaches the pull request, so the reviewer sees a proposed change that already passes checks. The engineer stays in control of what ships.

Find. Fix. Ship. Close.#

Hyrax reviews all code continuously, finds issues across security, correctness, maintainability, performance, architecture, and operations, and submits fixes as pull requests. Every fix arrives pre-verified, and a human approves each one before it merges. Nothing merges on its own.

The review-and-fix loop treats AI-written code the same as any other code, because both carry the same risk classes. Snyk Code and similar scanners find security flaws well. Hyrax adds a fixing layer that closes issues across a wider set of domains, so problems get resolved and not just flagged.

Hyrax makes your code better. Ship clean code.