A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection
This addresses the critical need for scalable and adaptive information security in AI systems, though it appears incremental as an improvement over existing defense frameworks.
The paper tackles the problem of defending LLMs against adversarial attacks and jailbreaks by introducing a real-time, self-tuning moderator framework that adapts quickly to new threats while maintaining a lightweight training footprint, with empirical evaluation on Google's Gemini models showing advantages over traditional methods.
Ensuring LLM alignment is critical to information security as AI models become increasingly widespread and integrated in society. Unfortunately, many defenses against adversarial attacks and jailbreaking on LLMs cannot adapt quickly to new attacks, degrade model responses to benign prompts, or introduce significant barriers to scalable implementation. To mitigate these challenges, we introduce a real-time, self-tuning (RTST) moderator framework to defend against adversarial attacks while maintaining a lightweight training footprint. We empirically evaluate its effectiveness using Google's Gemini models against modern, effective jailbreaks. Our results demonstrate the advantages of an adaptive, minimally intrusive framework for jailbreak defense over traditional fine-tuning or classifier models.