What is an AI code security scanner?
SHORT ANSWER
An AI code security scanner uses an AI model to review code for potential security weaknesses. It can also mean a scanner for AI-generated code, which need not use AI itself. SetRepo's Quick scan uses deterministic checks; its paid Deep scan adds AI review of selected code for access-control and business-logic issues. Both help you identify findings to investigate and fix.
What does “AI” mean in a security scanner?
Two different questions often share this name. One is whether a tool uses an AI model to review code. The other is whether it can check code produced by an AI coding assistant. Code can contain security weaknesses regardless of who wrote it, and deterministic scanners can check AI-generated code too.
SetRepo supports both deterministic scanning and an optional AI review. Its Quick scan uses rules and static analysis rather than an AI security review. A paid Deep scan adds review of selected code using Anthropic's API. This distinction matters when choosing coverage and deciding what code you are comfortable sharing with a model provider.
What SetRepo checks
A source-code scan can look for several kinds of evidence:
- Exposed credentials: keys and tokens in files and a limited portion of Git history. A detected secret is reported with its value redacted; SetRepo does not try the credential.
- Known vulnerable dependencies: package versions in supported manifests and lockfiles, checked against vulnerability records. A match does not by itself prove the vulnerable behavior is reachable in your app.
- Risky code and configuration: supported security patterns, route-level access control, and Supabase or Firebase rules present in the supplied code.
- Context that needs review: Deep scans add AI review of object-level access and business logic, plus a second opinion on uncertain critical and high pattern findings.
For example, a route can require a login and still return another customer's order. The important question is whether it checks ownership of the requested record. Read the IDOR guide for that distinction, and coverage and limitations for supported checks and gaps.
How an AI-assisted review works in SetRepo
- Provide a repository or ZIP archive and review the scan setup.
- Choose Quick for deterministic checks or Deep for those checks plus AI review. Review the credit estimate and maximum reservation before authorizing processing.
- On Deep scans, SetRepo selects relevant handlers, supporting code and data-model context for review. Secret values are redacted before that context is sent to Anthropic.
- Review the reported evidence and suggested fixes. Change the code in your own development workflow, test the behavior and rescan.
The model reviews selected context rather than the entire application. See how SetRepo handles your code for the data flow and retention details, or follow the GitHub scanning walkthrough to start.
Who benefits from this review?
Developers and small teams can use scanning as another review step before shipping or after changing sensitive features. Builders using AI coding tools can use the findings to focus their own code review. It is particularly useful to examine code that handles accounts, private records, payments or privileged actions.
What a scanner cannot conclude
A finding is a lead to confirm, and a clean report is limited to the checks that ran. AI can misunderstand intent or miss a dependency between code paths. SetRepo does not run your application, log in as different users or prove an exploit works. It does not certify that an app is secure.
Use separate accounts to test ownership and permissions yourself. For a deployed app, also check the live website. Choose a review appropriate to the sensitivity of the data and actions involved; automation is one part of that work.
Start a repository scan or compare Quick and Deep pricing.