AI Red Teaming

You can't attack what you don't understand — knowing how a model works reveals where its weaknesses come from.

Before testing you must know who the attacker is and what they're after — threat modeling directs all your effort.

The most critical LLM-app vulnerability: user input overriding the developer's instructions and hijacking the system.

Even aligned models can be tricked into producing disallowed content — knowing jailbreak methods is essential to test guardrails.

Models can leak training data or sensitive context — a direct privacy risk.

The model and components you depend on can be compromised before they reach you — a flaw here breaks the system at its root.

Tiny input perturbations can fool a model in ways humans wouldn't expect — knowing them is core to robustness testing.

An agent that can act vastly widens the attack surface — one injection can turn into real harmful action.

Manual testing can't cover everything — automation generates thousands of attacks and surfaces patterns you'd miss.

Finding a hole is half the job — your real value is proposing practical defenses that close it without breaking the app.

Your findings are worthless if they don't reach decision-makers — clear reporting and governance frameworks turn discoveries into real change.