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ReGA: Representation-Guided Abstraction for Model-based Safeguarding of LLMs

Authors: Zeming Wei, Chengcan Wu, Meng Sun

Published: 2025-06-02

arXiv ID: 2506.01770v1

Added to Library: 2025-06-04 04:02 UTC

📄 Abstract

Large Language Models (LLMs) have achieved significant success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks in generating harmful content and vulnerability to jailbreaking attacks. To analyze and monitor machine learning models, model-based analysis has demonstrated notable potential in stateful deep neural networks, yet suffers from scalability issues when extending to LLMs due to their vast feature spaces. In this paper, we propose ReGA, a model-based analysis framework with representation-guided abstraction, to safeguard LLMs against harmful prompts and generations. By leveraging safety-critical representations, which are low-dimensional directions emerging in hidden states that indicate safety-related concepts, ReGA effectively addresses the scalability issue when constructing the abstract model for safety modeling. Our comprehensive evaluation shows that ReGA performs sufficiently well in distinguishing between safe and harmful inputs, achieving an AUROC of 0.975 at the prompt level and 0.985 at the conversation level. Additionally, ReGA exhibits robustness to real-world attacks and generalization across different safety perspectives, outperforming existing safeguard paradigms in terms of interpretability and scalability. Overall, ReGA serves as an efficient and scalable solution to enhance LLM safety by integrating representation engineering with model-based abstraction, paving the way for new paradigms to utilize software insights for AI safety. Our code is available at https://github.com/weizeming/ReGA.

🔍 Key Points

  • Development of BitBypass, a novel black-box jailbreak attack that utilizes hyphen-separated bitstream camouflage to bypass safety alignments of LLMs.
  • The attack methodology diverges from traditional approaches by focusing on fundamental information representation as bits, rather than solely on prompt engineering or other adversarial techniques.
  • Evaluation of BitBypass against five state-of-the-art LLMs shows significant success in generating harmful and unsafe content, outperforming existing jailbreaking methods in terms of both stealthiness and attack success rate.
  • The research introduces a comprehensive evaluation framework for testing adversarial robustness of LLMs, highlighting the capabilities and vulnerabilities of aligned LLMs.
  • Creation of the PhishyContent dataset, aiding in the assessment of phishing-related content generation, establishing a more systematic approach to evaluate malicious prompt responses.

💡 Why This Paper Matters

The study of BitBypass contributes to the understanding of vulnerabilities in safety-aligned large language models. By introducing a novel attack strategy that exploits these weaknesses, this work emphasizes the ongoing challenges in ensuring the responsible deployment of LLMs, particularly in preventing the generation of harmful content. As AI continues to pervade various domains, insights gained from this research underscore the critical need for enhanced security measures and robust adversarial training methodologies.

🎯 Why It's Interesting for AI Security Researchers

This paper holds significant interest for AI security researchers as it thoroughly examines the adversarial landscape surrounding large language models, highlighting emerging vulnerabilities and providing empirical data on the effectiveness of sophisticated jailbreaking techniques. The insights gained can directly inform the development of more resilient AI systems and safety protocols, making it a crucial read for those focused on AI ethics, security, and robustness.

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