AI & LLM Security

What's included

• LLM red teaming: systematic attempts to break the model's guardrails, extract the system prompt, or make it say/do what it shouldn't.
• RAG pipeline audit: what data the model can leak if asked the wrong way, and whether real access control reaches all the way to the retriever itself.
• Review of tools and agents connected to the model (tool use / MCP): what the model can execute on its own, and what happens if malicious input manipulates it into abusing that tool.
• Indirect injection testing: malicious content hidden in documents, web pages or emails the model processes without the user typing it directly.
• Actionable report with concrete mitigations, prioritized by what's actually exploitable, not a generic OWASP-for-LLM checklist.

How we work

We treat your AI system as any other attack surface: we define what the model can see and do, then actively probe how it can be pushed outside those limits. Not a theoretical checklist, real exploitation against your actual implementation.

Who this is for

Companies that already have (or are about to launch) a generative AI product, chatbots with access to internal data, or agents with execution permissions on real systems.