A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection

2025-08-09

AI Summary

To address the lagging defense against adversarial prompting and jailbreaking attacks on large language models (LLMs), which compromises benign response quality and incurs high deployment overhead, this paper proposes a real-time, self-tuning lightweight auditing framework. Departing from conventional fine-tuning or static classifiers, our method introduces a dynamic, adjustable auditing module that jointly integrates adversarial sample detection with feedback-driven online parameter optimization, enabling millisecond-level adaptive updates of defense policies. Experiments on the Gemini model demonstrate robust resistance against state-of-the-art jailbreaking techniques—including Ghost, Multistep, and Suffix—achieving a false positive rate below 2.1%, an average latency increase of less than 8 ms for benign prompts, and negligible degradation in BLEU and FactScore (both <0.5%). Our core contribution is the first realization of real-time safety alignment that is low-overhead, high-fidelity, and continuously evolvable via online adaptation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Detects adversarial prompts in real-time for LLMs
• Adapts quickly to new attacks without degrading performance
• Maintains lightweight training for scalable implementation

Innovation

• Real-time self-tuning moderator framework
• Lightweight training footprint defense
• Adaptive minimally intrusive framework