Even the most carefully crafted system prompts can “go rogue,” reverting to generic assistant mode or leaking hidden instructions, undermining security, consistency, and user trust. Drawing on hard-earned lessons from building a goal-oriented AI group chat platform, this session delivers:

* Multiple prompt-leakage and prompt-reversion examples showcasing real-world LLM failures
* Live demos of evaluation workflows, detecting and analysing rogue or unexpected responses in real time
* Practical security patterns for prompt engineering to mitigate leakage and fallback risks
* Techniques for adding nondeterministic evaluation tests into your deployment pipeline

This no-fluff, demo-driven talk equips engineers and security practitioners with battle-tested patterns to keep LLM-powered applications on-brand and secure. You’ll leave with open-source repos, threat-model templates, and actionable takeaways to implement immediately.