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

Authors: Ivan Zhang

Published: 2025-08-10

arXiv ID: 2508.07139v1

Added to Library: 2025-08-14 23:10 UTC

Red Teaming

📄 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.

🔍 Key Points

  • Introduces a novel Real-Time, Self-Tuning (RTST) moderator framework for enhanced LLM adversarial prompt detection, which can self-adjust with minimal computational overhead.
  • Demonstrates clear advantages of RTST over traditional methods (like fine-tuning) in effectively mitigating jailbreak attacks and enhancing model safety.
  • Empirical evaluation using Google's Gemini models shows significant improvements in Attack Success Rate (ASR) and Refusal Rate (RR), indicating a more reliable defense against adversarial inputs.
  • The two-agent system design maximizes adaptability and user control while facilitating real-time learning, representing a major step forward in LLM security methodologies.
  • Ablation tests confirm that real-time optimization positively impacts the framework’s performance and reinforces the methodology's efficacy in dynamic environments.

💡 Why This Paper Matters

This paper is crucial in the field of AI security as it presents an innovative approach to enhancing the safety of large language models against adversarial attacks. The RTST framework not only tackles existing vulnerabilities but also adapts to new threats in real-time, making it a valuable contribution to the ongoing development of robust AI systems. Its lightweight and user-friendly design allows for broader applicability across different AI applications.

🎯 Why It's Interesting for AI Security Researchers

This paper is highly relevant to AI security researchers as it outlines a new framework that directly addresses the pressing challenges of adversarial attacks on large language models. By showcasing empirical results and an adaptive design, it encourages further exploration of real-time defenses and self-tuning mechanisms, inspiring future research and development in AI security.

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