I review security boundaries in AI agents, MCP integrations and tool-enabled applications. My background is in Web3 security: smart contracts, bridge flows and accounting invariants.
Method: map the flow → define invariants → build test scenarios → inspect results → verify fixes.
I use AI for code analysis, test development and automation. Reports distinguish confirmed behavior, security impact and remaining gaps.
- Voxel Bench — ETHOnline 2026: project participation and the publicly credited SR-01 recipe-authorship finding; the team implemented the fix.
- Web3 case collection: smart-contract assessment notes and evidence.
- FastAPI mutation benchmark: an independent static auditor identified all five deliberately introduced defects. Attack scenarios were not validated over HTTP.
- AppSec boundary workflow — a defensive FastAPI lab that turns an authorization matrix into pytest checks for note ownership, request validation, and token revocation. The runner also checks one Semgrep rule and saves Markdown, JSON, and JUnit reports. 14 tests passed locally. A GitHub Actions workflow is configured and awaits its first hosted run.
- Browser Use: navigation-policy behavior; security impact remains unconfirmed.
- FastMCP / MCP SDK: SSE error propagation and local verification of a proposed upstream fix.
- Learn with GenAI: application resource-cleanup bug, local patch and regression tests.
Open to remote AI security / AppSec roles and contract research.