Choose a tool by what it catches in your own repositories, where it runs, and how it handles volume. AI assistants produce code faster than people review it, so the tool must work inside the editor and the pull request, cover dependencies as well as source, and keep false positives low enough to read.
Is AI-generated code less secure than human code?
Do not assume either direction. NIST published SP 800-218A, a community profile that extends the Secure Software Development Framework to generative AI and dual-use foundation models. Its message for developers is practical: the practices you already follow still apply, with added attention to the model, its training data and the code it emits.
So the question is less "is it worse" and more "what changes". Three things do. Volume rises, because one developer can propose ten times as many lines. Provenance blurs, because nobody remembers why a line exists. And suggestions can include packages that you would not have picked.
What is different about the failure modes?
Language models predict plausible code, not verified code. Typical problems to expect include missing input validation, outdated API usage, hard-coded example credentials copied from training patterns, and dependency suggestions pinned to old versions.
A less discussed one is the invented package. A model may suggest an import for a library name that does not exist. If an attacker registers that name first, a developer who installs without checking pulls in attacker code. Treat any new dependency in a generated change as needing the same review as one a colleague proposed.
Which capabilities should the tool have?
Use these as a test, not a feature list.
Where it runs. A scan that only runs weeks later in a nightly job misses the moment the code is cheapest to fix. Look for editor and pull request integration.
What it covers. Source code analysis, dependency analysis and secret detection each catch different things. A tool that covers one leaves gaps in the other two.
How it ranks. A list of 900 findings is not a result. Ask whether the tool can tell you which vulnerable dependencies your code actually calls, since that cuts the list sharply.
What it does with a fix. Suggestions are fine. Automatic changes need a human gate. Ask whether automated fixes are opt in and arrive as reviewable pull requests.
Where the code goes. Some tools upload source to a vendor. Others run locally. Know which one you are buying, especially for regulated code.
Control over its own AI. If the tool uses a model, ask whether you can turn the model off or bring your own.
How should you run the trial?
Build a small, honest test set from your own history rather than a vendor demo repository.
- Pick three repositories of different age and language.
- Include a few known past defects you already fixed, such as an injection bug or a leaked key.
- Add a constructed pull request where an assistant generates a login handler. Check whether the tool flags weak password handling or missing rate limiting.
- Record what each tool found, what it missed, how long it took and how many findings a developer would have to dismiss.
A constructed example: you generate a file upload endpoint with an assistant and the code trusts the client supplied file name. A useful tool flags path traversal in the pull request, names the line and explains the risk in plain words. A poor one stays silent or buries it among forty style warnings.
What about the assistant itself?
Governance matters as much as scanning. Decide which assistants are approved, what data they may see, and whether generated code is labelled in commits so reviewers know to look harder. OWASP's Top 10 for LLM applications is a good checklist for the risks of the assistant itself, such as prompt injection and insecure output handling.
Safeguard offers IDE extensions for VS Code, Cursor via Open VSX and JetBrains, plus an MCP server so an assistant can query security context while it writes. The free scan at gold.safeguard.sh/scan lets you try dependency checks on up to 100 packages without installing anything.
How to check or apply this
- List where AI-generated code enters: IDE, chat, agent, pull request bot.
- Write down which checks run at each point.
- Run the trial above on your own repositories.
- Score tools on findings you would act on, not total findings.
- Require a human review gate for any automated fix.
- Re-run the trial every six months, since both models and tools change.
Quick check
Do I need a separate tool just for AI-generated code?
Usually not. You need coverage that runs early enough and fast enough for the volume. The code origin matters less than where and when it is checked.
What is a package hallucination?
It is when an assistant suggests a dependency name that does not exist. An attacker can publish a package under that name, so verify every new dependency before installing.
Sources
- NIST SP 800-218A: Secure Software Development Practices for Generative AI and Dual-Use Foundation Models
- NIST SP 800-218: Secure Software Development Framework
- OWASP Top 10 for LLM Applications
Last reviewed: 18 October 2026.
