Why do AI-generated code suggestions need a special security review?

LLMs reproduce patterns from their training data, including insecure ones, and they optimise for code that looks plausible and runs — not code that is secure. Common results are hardcoded secrets, string-built SQL, missing input validation, and outdated or hallucinated dependencies, all of which pass a quick glance but fail a security review.

What is dependency confusion and why does it matter for AI code?

Dependency confusion is when a package name an AI suggests does not exist (or is a malicious squat) on the public registry, so installing it pulls attacker-controlled code. AI tools frequently hallucinate plausible package names, so every imported dependency in AI code must be verified against the real registry.

Does ticking everything mean the code is secure?

It means you have consciously checked the most common AI-specific failure modes, which catches the majority of issues. It is not a guarantee — pair the checklist with automated SAST, dependency scanning, and a human reviewer who understands the code's purpose.

Does my code get uploaded?

No. This is a static checklist that runs entirely in your browser. You review your code locally against the items; nothing is uploaded, scanned remotely, or stored.

Can I use this for human-written code too?

Yes — every item is good practice for any code review. It simply emphasises the failure modes that appear disproportionately often in AI-generated suggestions, so it is most valuable when reviewing model output before merge.

AI-Assisted Code Review Security Checklist

Review AI code for the mistakes models actually make

AI coding assistants are fast and confidently wrong about security. They paste secrets inline, build SQL with string concatenation, skip input validation, and import packages that do not exist on the real registry. This checklist concentrates on those AI-specific failure modes so you can review a suggestion before merging it. It is fully client-side — you check items against your own code; nothing is uploaded.

How it works

You choose the language or framework and the type of code you are reviewing. The tool surfaces a security checklist weighted toward the issues that appear disproportionately in model output — hardcoded secrets, injection (SQL, command, template), insecure deserialization, missing authn/authz checks, weak crypto/randomness, and dependency confusion. Each item explains what to look for. As you tick items, a progress indicator shows how complete the review is. The rule is simple: do not merge until every relevant item is resolved.

Tips and notes

Verify every dependency. Confirm each imported package exists on the real registry and is the one you intend — AI hallucinates names.
Hunt for secrets first. Models love to inline keys “for the example.”
Trust nothing from input. Check that user input is validated and queries are parameterised.
Layer with tooling. Pair this with SAST and dependency scanning; the checklist guides the human, the scanners catch the rest.