When your product is AI, your product is the attack surface. Penetration testing for AI companies covers the model endpoints, the agents and tools they reach, and the retrieval pipelines behind them, alongside the conventional application surface, so an attacker cannot make your own system work against you.
AI products carry a class of exposure conventional testing misses, because the system reads untrusted content and acts on it.
Direct and indirect injection through every channel the model reads, turning documents, pages, and tool output into instructions your system follows.
Agents that call tools, browse, or reach internal APIs, and the excessive agency that turns an injection into a real action against your systems.
Poisoning the corpus, cross tenant reads in the vector store, and data leaving through model output, alongside the model endpoints themselves.
Model backed features ship in weeks, and the security discipline around them is still being written. Planck Operator tests them the way an adversarial user would, mapped to the OWASP Top 10 for LLM Applications and extended to the tools, retrieval, and APIs your agents reach.
It covers the conventional application and API surface in the same engagement, because some of the highest impact findings are classic flaws the model can be made to reach.
Because your product is the attack surface. An AI company ships models, agents, and RAG pipelines that read untrusted content and take actions, a class of risk conventional test plans were not written for. Testing has to cover prompt injection, tool abuse, and data exfiltration alongside the usual application surface.
The model endpoints, the agents and tools they can reach, the retrieval pipelines and vector stores, and the application around them. It covers the OWASP Top 10 for LLM Applications plus the agentic risks of a system that acts.
Yes. A normal test asks what an attacker can read or change. For an AI product, it also asks what an attacker can make your model or agent do: invoke tools, leak other users' context, or act on injected instructions. That requires adversarial, AI native testing.
Describe your AI product and the tools it can reach, and we will show you what an attacker can make it do.