LLM red teaming

LLM red teaming applies the mindset of a penetration test to a model-backed application. The team probes the whole surface: whether the model can be jailbroken past its policy, whether the application can be hijacked by prompt injection, whether sensitive data (training data, the system prompt, other users’ content) can be extracted, and whether the model produces output that is harmful or actionable against the business. Testing is structured but exploratory: an operator iterates, chains partial successes, and follows the model’s behaviour rather than running a fixed checklist, because model responses are non-deterministic and the interesting failures emerge from the specific application, not from a generic prompt.

The common misstep is scoping the exercise to the model in isolation, as if the risk were a bad answer. The risk that matters is the application: a model with tools, memory and access to data, deployed with permissions that assume it will only ever be helpful. An attacker does not care whether the model “understands” it is being manipulated; they care what it is allowed to do once it complies. In practice the highest-impact findings are architectural, and a red team that only counts refused-versus-answered prompts misses them entirely. The value is in demonstrating a full path from a crafted input to a real, unwanted outcome.

We deliver an application-level report mapped to a recognised framework, the OWASP Top 10 for LLM Applications, with each finding tied to a reproduction: the input, the model behaviour, and the downstream impact. Existing guardrails are recorded with their bypass rate, and severity is set by what a successful attack reaches, not by whether a single prompt worked. This is how we run an AI red team engagement, scoped to the deployed system rather than the model alone.