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Research Paper | Computer Science and Engineering | Volume 15 Issue 4, April 2026 | Pages: 115 - 120 | India
Security Challenges of Large Language Models: Adversarial Threats, Governance Gaps, and Organizational Risks
Abstract: Large Language Models, such as ChatGPT, Gemini, and Perplexity, have rapidly integrated into academic, industrial, and executive workflows. Although they enhance productivity and decision- making processes, these models engender novel cybersecurity vulnerabilities that traditional security frameworks fail to address effectively. Unlike deterministic von Neumann architectures, LLMs constitute stochastic, centralized systems pretrained on vast datasets and iteratively refined through ongoing human-AI interactions. This paper scrutinizes the profound reconfiguration of the cybersecurity threat landscape induced by LLM proliferation, emphasizing data exfiltration, adversarial perturbations, user behavioral patterns, and governance gaps. Employing conceptual analysis reinforced by empirical survey data, it exposes deficiencies in stakeholder awareness and organizational resilience. The implications underscore the need for hybrid techno-administrative paradigms to support robust, ethically aligned LLM deployments. Moreover, this study systematically delineates the security and privacy risks intrinsic to LLMs, classifying vulnerabilities, misuse-induced harms, extant mitigation approaches, and their constraints.
Keywords: Large Language Models, Cybersecurity, Adversarial Attacks, Data Leakage, AI Governance, Prompt Injection, Data Poisoning
How to Cite?: Animesh Sachin Thakur, Rameshwari Patil, Jatin Sundrani, "Security Challenges of Large Language Models: Adversarial Threats, Governance Gaps, and Organizational Risks", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 115-120, https://www.ijsr.net/getabstract.php?paperid=SC26211113715, DOI: https://dx.dx.doi.org/10.21275/SC26211113715