AI and LLM Security Testing
We validate prompt injection, unsafe tool execution, sensitive data exposure, model-to-system trust assumptions, and the controls that should limit harmful actions.
What teams usually value in this service
We test whether untrusted content can manipulate the model into running actions or exposing data beyond intended boundaries.
Retrieval, memory, and response construction are reviewed for ways one user’s context could bleed into another user’s outputs.
Where the model can call tools or trigger downstream actions, we validate how authorization and execution safety are actually enforced.
Related resources
AI security testing should cover prompt injection, tool use, data exposure, orchestration logic, and how the rest of the system trusts model output.
A real pentest should validate exploit paths, business logic, and impact. It should not stop at scanner output or generic severity labels.
No. Prompt testing matters, but effective AI security testing also looks at tools, data access, orchestration logic, and how the rest of the system trusts model output.
Yes. Pre-launch review is often the right time because it lets teams fix unsafe design assumptions before customers depend on the workflow.