500 structured security scenarios across 20 AI-specific domains, run by a fleet of specialized security agents.
Generic penetration testing wasn’t built for LLMs, RAG, agents, MCP and multi-agent systems. Assessing modern AI needs its own taxonomy, its own specialized attack agents, and an evidence-first, authorization-gated process end to end — not a checklist borrowed from conventional AppSec.
20-domain, 500-scenario AI security taxonomy — prompt injection, RAG poisoning, agent goal hijacking, MCP security, memory security, multi-agent/A2A, multimodal, supply chain, governance and more
Dynamic threat modeling that selects the relevant specialized agents per target
Parallel, bounded-concurrency execution of approved scenarios only, fail-closed by default
Immutable evidence collection mapped to OWASP LLM Top 10 and MITRE ATLAS
Attack-path correlation with explainable, framework-mapped risk scoring
Executive and technical reporting with tracked remediation and retesting
Designed and reviewed against all ten — see how I evaluate every architecture.
I design and ship systems like this one — from architecture through to production.
