The unprecedented proliferation of Large Language Models (LLMs) has fundamentally transformed the technological landscape, shifting artificial intelligence from isolated research environments into critical enterprise deployment. However, the integration of generative, probabilistic AI models into deterministic software architectures introduces a novel and highly complex attack surface. Traditional cybersecurity paradigms which rely on strict input validation, rigid access controls, and predictable computational pathways are inadequately equipped to secure systems where natural language serves as both data and executable instruction. This paper provides an exhaustive methodological analysis of the LLM security landscape. It systematically dissects structural vulnerabilities, including direct and indirect prompt injection, training data poisoning, model inversion, and insecure output handling. Furthermore, it explores advanced defensive methodologies, ranging from semantic guardrails and adversarial Reinforcement Learning from Human Feedback (RLHF) to hardware-level Trusted Execution Environments (TEEs) and Differential Privacy algorithms. By synthesizing technical vulnerabilities with governance frameworks like the OWASP Top 10 for LLMs, this research establishes a robust, defense-in-depth blueprint essential for securing the next generation of artificial intelligence infrastructures.

Mohammed Zameel PF, Riya Jyothish, L. C. Manikandan, "Architectural Vulnerabilities and Defensive Methodologies in Large Language Models: A Comprehensive Security Paradigm" International Journal of Scientific Research and Engineering Development, V9(5): Page(586-592) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.