Security Assessment and Layered Defense Framework for Large Language Models: Findings and Future Perspectives

Large Language Models (LLMs) have revolutionized intelligent automation and content creation in a variety of fields. However, significant security issues as quick injection, jailbreak attacks, the creation of false information, and data leakage have been brought about by their growing use. The adversarial vulnerabilities of LLMs, the classification of exploitable behaviors, and the creation of a preventive security architecture are all systematically investigated in this study. The study uses experimental red-teaming and quantitative assessment criteria to evaluate typical open-source and closed-source approaches. Harmful behaviors are categorized into technological, behavioral, and operational categories using unsupervised clustering approaches. To lower security risks during deployment, a layered defense architecture with input sanitization and runtime monitoring is suggested. The results show that modern LLMs are still vulnerable to a variety of hostile assaults and need multi-layered security measures for secure andHowever, significant security issues as quick injection, jailbreak attacks, the creation of false information, and data leakage have been brought about by their growing use. The adversarial vulnerabilities of LLMs, the classification of exploitable behaviors, and the creation of a preventive security architecture are all systematically investigated in this study.