AIMoat
A scanner that inspects how AI chat products are configured in the browser, runs targeted probes, grades findings, and produces remediation-oriented evidence.
The system behind the interface.
AI security claims are easy to overstate. A useful scanner has to distinguish public configuration evidence from platform vulnerabilities and present reproducible findings.
Architecture choices that shaped the product.
The scanner focuses on what a browser and an unauthenticated visitor can observe instead of claiming access to private model or platform internals.
A grade is never the only result. Findings retain the request, response, browser signal, or configuration detail that produced them.
Headers and exposed data need deterministic inspection; model-behaviour risks need carefully scoped interaction probes.
What exists beyond the concept.
- Static checks across scripts, browser storage, headers, and exposed endpoints
- Dynamic prompt-injection and prompt-extraction probe corpus
- Severity scoring with masked and full-evidence report modes
A production-minded result.
A working scanner engine tested against public chatbot integrations, with platform detection, multiple check modules, attack probes, and report generation.