Your AI stack is attack surface.
Offensive assessments of LLM applications, agentic systems, and Model Context Protocol deployments - grounded in our own published research on enterprise MCP governance.
LLM application testing
Assessment of applications built on large language models against prompt injection (direct and indirect), jailbreaking, system prompt extraction, insecure output handling, and sensitive data leakage - mapped to the OWASP Top 10 for LLM Applications.
Agentic AI & tool-use assessment
AI agents that can call tools, browse, and act on your systems introduce a fundamentally larger attack surface than a chatbot. We test tool-abuse paths, confused-deputy scenarios, cross-context data exfiltration, and the blast radius of a compromised agent inside your environment.
MCP deployment review
Model Context Protocol servers are becoming the standard bridge between AI and enterprise data. We assess MCP server configurations, authentication and scoping, audit logging, and rollout governance - an area where our team has presented original research to the National Cyber Security Authority of Albania (AKSK).
AI supply chain review
Review of the models, packages, MCP servers, and third-party integrations your AI features depend on - provenance, update channels, and the compromise scenarios that follow from each dependency.