This research creates a systematic framework to understand and classify the growing landscape of adversarial attacks against Large Language Models.

• Attack Taxonomy: Categorizes LLM vulnerabilities through the lens of attack objectives and methodologies
• Threat Landscape: Maps current attack vectors threatening LLM privacy, reliability, and trustworthiness
• Defense Mechanisms: Evaluates existing countermeasures and highlights security gaps requiring attention
• Future Challenges: Identifies emerging threat vectors as LLMs become more integrated into critical systems

This research is vital for security professionals as it provides a comprehensive understanding of LLM vulnerabilities, helping organizations implement effective safeguards before deploying AI systems in sensitive environments.

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives