# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 1 Introduction
The present paper builds upon their work by offering a more exhaustive analysis of prompt injection attacks, incorporating the latest developments in the field, and presenting practical, actionable solutions tailored to the risks associated with each CIA dimension. Similarly, [PERSON] et al. [5] provided a preliminary taxonomy, but did not utilize the structured approach offered by the CIA triad. By adopting this robust framework and integrating recent research, our study addresses these gaps and provides a strong foundation for advancing LLM security. Other key works have focused on specific vulnerabilities rather than presenting a comprehensive approach; for example, [PERSON] et al. [6] analyzed practical vulnerabilities in real-world LLM systems, while [PERSON] et al. [7] explored automated methods for creating universal adversarial prompts. These studies highlighted the sensitivity of LLMs to crafted inputs, revealing critical systemic vulnerabilities; however, they did not propose a unifying framework for understanding and mitigating these threats. Additionally, [PERSON] et al. [8] focused on indirect attacks, revealing how seemingly benign inputs can exploit LLMs' assumptions about input context, leading to unintended behaviors. Similarly, [PERSON] et al. [9] provided a comparative evaluation of vulnerabilities across architectures and emphasized the importance of multilayered defenses. While these works advanced the understanding of LLM vulnerabilities, they did not offer the structured classification or mitigation strategies proposed in this paper. Together, these studies represent a shift from isolated analyses to more systematic evaluations of prompt-based vulnerabilities. However, gaps remain in providing an integrative framework capable of addressing the complexities of modern LLM deployments. The present paper bridges these gaps by presenting a novel taxonomy rooted in the CIA triad. It not only categorizes prompt injection attacks comprehensively, but also links these classifications to tailored mitigation strategies, providing researchers and practitioners with actionable tools to enhance the security of LLMs in critical applications. Table 1 provides a detailed comparison of these influential studies, highlighting their focus, methodologies, and contributions to the understanding of prompt injection attacks. This analysis demonstrates the unique contributions of this study, particularly its ability to address key shortcomings in the existing literature. By combining structured classification with practical mitigation strategies, this paper represents a significant advancement in securing LLMs and ensuring their reliable deployment in high-stakes environments. The rapid proliferation of Large Language Models (LLMs) across various industries has introduced significant security concerns, particularly around adversarial prompt attacks. While prior studies have explored different aspects of LLM security, they often focus on isolated threats rather than providing a structured integrative framework for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
understanding and mitigating these risks. This gap necessitates a systematic approach towards classifying and addressing prompt-based vulnerabilities. To address these challenges, this paper makes the following key contributions:  
* Comprehensive Taxonomy of Prompt Attacks--We introduce a structured classification of prompt-based attacks using the Confidentiality, Integrity, and Availability (CIA) triad. This taxonomy provides a systematic way of understanding how adversarial prompts impact LLM security. * Analysis of Emerging Threats--Unlike prior studies that provided only a general overview, we offer an in-depth examination of the latest adversarial attack techniques, highlighting their mechanisms, real-world implications, and potential impact on LLM-integrated systems.