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This is a Experimental Study study.

October 10, 2025

Securing Large Language Models Against Jailbreaking Attacks: A Novel Framework for Robust AI Safety

Key Points

The proposed framework significantly improves AI safety by defending against jailbreaking attacks on language models.
Utilizing adversarial training, the framework enhances LLMs' resilience against malicious attempts to bypass security.
Real-time anomaly detection is key to maintaining safety and integrity in large language models.
The innovative SAFE-LLM system presents a multi-layered security approach, contributing to advancements in robust AI safety.

Abstract

Large Language Models (LLMs) like ChatGPT, GPT-4, and Claude have revolutionized natural language processing, but they are increasingly vulnerable to adversarial "jailbreaking" attacks that bypass safety protocols. This paper proposes a novel multi-layered security framework to defend LLMs against jailbreaking techniques using a combination of adversarial training, behavior watermarking, and real-time anomaly detection. We present an innovative system called SAFE-LLM (Security-Aware Fine-tuned & Encrypted Language Model) to achieve robust AI safety.

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