Adversarial Threats and Vulnerabilities in Large Language Models

Abstract

Large Language Models (LLMs) have revolutionized natural language processing by enabling advanced text generation, contextual reasoning, and decision support across domains. However, their widespread adoption has also exposed them to adversarial threats and vulnerabilities that pose significant security, ethical, and operational challenges. Attack vectors such as data poisoning, prompt injection, model inversion, and evasion techniques highlight the fragility of LLMs when confronted with malicious actors. These vulnerabilities can lead to privacy breaches, misinformation propagation, biased outputs, and systemic exploitation of model behavior. Understanding adversarial threats is therefore crucial for safeguarding the reliability, trustworthiness, and resilience of LLMs in critical applications. This chapter provides an in-depth examination of common adversarial techniques, their underlying mechanisms, and the risks they introduce. It also explores the intersection of security, privacy, and robustness in LLM deployment, offering a foundation for developing defense strategies and future research directions