Large language model guardrails are designed to prevent unsafe, non-compliant or unsupported outputs, but static test sets and hand-written jailbreaks cannot cover the full range of interactions with a live AI system. This demonstration invites attendees to try to "break" a privacy guardrail and see how adversarial testing can reveal weaknesses that conventional evaluation may miss.

Based on CommBank AI Labs' "Break My Model" challenge and related research into self-improving adversaries, participants submit prompts containing private information such as names, dates of birth, card numbers and government identifiers, with the aim of getting them past the model undetected. The demonstration shows whether the information has been detected and explains what the result reveals about guardrail behaviour.

Alongside human attempts, attendees will see an automated attacker that learns to generate increasingly effective attacks, demonstrating how reinforcement learning can move red teaming beyond fixed prompt libraries. The session will also explore over-refusal, showing how a guardrail can successfully block unsafe content while incorrectly rejecting legitimate requests. Attendees will gain a practical understanding of how adversarial testing, independent evaluation and human review can be combined to improve AI reliability, safety and customer outcomes.