Embed, resell, or white-label AI security — OEM, scanner, MSSP, consulting, and reseller tracks are open now
A.Team adapter
Outcome-oriented proof text for project entries and profile summaries.
Canonical copy
AI Product Security Architect / Browser Security Researcher Confidential AI Automation Platform Conducted a deep product-security assessment of browser trust boundaries across native and agentic browser surfaces, including a privacy-focused Windows desktop browser built on WebView2 and .NET. The work covered privileged internal page handling, native bridge exposure, host-object registration, origin gating, script-injection persistence, credential-surface protection, and native command dispatch — and translated those findings into a reusable defensive framework for AI-enabled automation products. 8 structured finding areas documented in the anonymized assessment model.
Public-safe caveat
This case study uses public-safe and anonymized language. It avoids sensitive exploit payloads, confidential vulnerability-handling details, unpublished proof-of-concept steps, and any claim that would disclose unresolved or restricted security information.