Prbl vs CodeQL for AI-generated code

CodeQL is a powerful semantic analysis engine that treats code as data you query. It is deep and flexible, but it rewards expertise. Prbl is turnkey and opinionated, tuned out of the box for the flaws AI coding tools produce.

Feature	Prbl	Them
Custom query flexibility	Opinionated rules, no queries	Core strength, query-driven
Learning curve	None, paste and scan	Steep, QL language
AI-generated file detection	Yes, built for this	No
AI-scaffolding vulnerability classes prioritized	Yes, out of the box	Depends on queries used
Live-URL exposure check	Yes	No
False-positive posture	Tuned under 10 percent on AI-code classes	Depends on query tuning
Auto-fix for findings	Yes, AI rewriter	No
Pricing entry point	Free, $29/mo Pro	Free for open source, GHAS for private

Where CodeQL wins

CodeQL is one of the most powerful analysis engines available. If you have security engineers who can write and maintain queries, you can model almost any vulnerability pattern precisely across a large codebase. For deep, custom analysis, its ceiling is very high.

Where Prbl is different

That power comes with a query language and real expertise to use well. Prbl needs none of that. It assumes an AI tool wrote the code and ships tuned for the result: hardcoded secrets in scaffolding, missing authentication on generated routes, fallback secrets in environment lookups, and Broken Object Level Authorization. Paste a repo or URL and get a short, high-signal list in seconds, with an AI rewriter to fix each finding.

Which to choose

CodeQL suits teams with security engineers who want to author custom analysis. Prbl suits teams shipping quickly with AI assistants who want specific answers now without writing a single query. If you already run CodeQL through GitHub Advanced Security, Prbl adds AI-code-specific signal on top.