Your chatbot passed its penetration test. That tells you the API and servers are sound — not whether the model can be talked into leaking data or calling a tool it should not. That is a different test.
Code, APIs, authentication, infrastructure and cloud configuration around the model.
Prompt injection, data leakage, excessive agency, unsafe output and jailbreaks.
Any LLM feature that touches customer data or can take actions needs both, on a cadence.
An LLM application has two attack surfaces. One is conventional: the web front end, APIs, authentication, databases and cloud infrastructure. The other is the model’s behaviour: what it can be persuaded to reveal, generate or do through language, documents and connected tools. VAPT tests the first; AI red teaming tests the second.
AI red teaming is structured adversarial testing of the model in context, typically mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS:
A traditional application with no AI features needs VAPT. An internal LLM tool with no sensitive data and no actions may start with lighter AI evaluation. Any LLM feature that touches customer data, retrieves internal documents or can call tools needs both — and because model, prompt and tool changes alter behaviour, AI red teaming belongs in a recurring evaluation cycle, not a one-off.
The two can be scoped together: VAPT across the application and infrastructure, AI red teaming across the model, retrieval and tool layer, with findings reported against one risk register. Our AI Agent Guardrail & Red-Team Checklist is a practical starting point, and our AI Testing & Evaluations practice runs the adversarial side.
No. VAPT tests code and infrastructure flaws. Prompt injection, data leakage through the model and unauthorised tool use require AI red teaming against the model, its prompts, retrieval and tools.
Automated attack libraries add breadth, but human testers are still needed to find context-specific and multi-step attacks, particularly against agents with tool access.
Yes. They can be scoped together, with VAPT on the application and infrastructure and AI red teaming on the model, retrieval and tool layer, reported in one risk register.
Whenever the model, system prompt, retrieval sources or tool permissions change materially, plus a regular cadence — behaviour shifts with each of those changes.