Why AI features need their own security test
An LLM treats instructions and data as the same thing: text. That creates attack classes, such as prompt injection, that traditional web and API testing does not look for. When a model can read internal documents or call tools on a user's behalf, a successful attack can expose data or take actions you never intended.
Most pentest scopes do not include LLM behavior by default. If your last test covered your web app and API but not the AI layer, that layer has likely never been tested.
What we test
We work through the risk categories in the OWASP Top 10 for LLM Applications, prioritizing the ones that apply to your design.
- Direct prompt injection and jailbreaks that bypass your instructions and safety controls
- Indirect prompt injection hidden in web pages, documents, emails, or other content the model reads
- Sensitive information disclosure, including system prompt leakage and exposure of other users' data
- RAG weaknesses: retrieval that ignores user permissions, poisoned documents, and vector store exposure
- Excessive agency: agents and tool integrations that can be tricked into unauthorized actions
- Improper output handling, where model output reaches browsers, databases, or code without validation
- Unbounded consumption that can drive up cost or degrade service
Who needs AI security testing?
Software companies adding chat assistants, copilots, or agents to their products; organizations deploying internal AI tools connected to company data; and anyone whose customers have started asking about AI security in vendor reviews.
If you also need to meet AI governance requirements, such as the EU AI Act, testing results provide useful evidence alongside your risk management documentation.
