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.
Frequently asked questions
Do I need to know CodeQL's query language to use Prbl?
No. CodeQL requires writing or maintaining QL queries to get value. Prbl ships pre-tuned for AI-generated code patterns with no queries required. Paste a repo or URL and get results in seconds.
Is Prbl as powerful as CodeQL?
Not in the same way. CodeQL's semantic dataflow analysis has a very high ceiling for security engineers who can author custom queries. Prbl trades that flexibility for zero setup and a sharp focus on the specific ways AI coding tools fail.
I already use CodeQL through GitHub Advanced Security. Do I still need Prbl?
If you rely heavily on Cursor, Copilot, Lovable, or Claude Code, yes. CodeQL's queries were not written assuming AI-generated code; Prbl adds that specific signal on top.