Securing Large Language Models Against Jailbreaking Attacks: a Novel Framework for Robust Ai Safety
Year: 2025
Type: article
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.
DOI: 10.52152/801808
Authors: Ms. Archana Budagavi, Mrs. Anushya V P, Mrs. Subhashree D C, Dr. Jagadish R M, Dr. Girish Kumar D, Ms. M M Harshitha
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414948873
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