# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 1 Introduction  
Advancements in Artificial Intelligence (AI) have significantly transformed Natural Language Processing (NLP), particularly through the development of Large Language Models (LLMs). These models have vastly improved machine capabilities in understanding, generating, and responding to human language. Through their deployment across a wide range of applications from customer service automation to critical fields such as healthcare, engineering, and finance, LLMs are currently revolutionizing how systems interact with human users and offer domain-specific knowledge [1]. However, as the adoption of LLMs increases, security concerns have become more prominent, particularly the risks associated with adversarial manipulations known as prompt attacks. Prompt attacks exploit the sensitivity of LLMs to crafted inputs, potentially leading to breaches of confidentiality, corrupted outputs, and degraded system performance. For instance, attackers can manipulate LLMs by injecting malicious instructions into input prompts to override intended behaviors and induce harmful or misleading responses. These vulnerabilities pose significant risks, especially in sensitive environments where trust, accuracy, and reliability are paramount [2]. Prompt engineering is a critical technique for optimizing the performance of LLMs, but has become a double-edged sword; while it allows users to refine and enhance modeloutputs, it also creates opportunities for adversarial manipulation. In particular, prompt injection attacks exploit this interface by inserting crafted commands that mislead the model into generating unintended responses. Such attacks can result in leakage of sensitive information, dissemination of biased or harmful content, and loss of trust in LLM-integrated systems. In critical applications such as engineering decision support systems or financial advisory tools, these attacks can lead to real-world harms including misinformation, regulatory violations, and reputational damage [3]. Despite the growing prevalence of prompt attacks, existing research has largely focused on improving the performance and capabilities of LLMs, with limited attention paid to the security vulnerabilities introduced by adversarial manipulations. There is a critical need for a systematic examination of these vulnerabilities in order to understand their mechanisms, classify their impact, and propose effective mitigation strategies. This is particularly important given the increasing role of LLMs in critical environments where even minor security lapses can have disproportionate consequences. This paper addresses a significant gap in the field by introducing a comprehensive taxonomy of prompt attacks that is systematically aligned with the Confidentiality, Integrity, and Availability (CIA) triad, a well-established framework in cybersecurity. By mapping prompt attacks to the dimensions of the CIA triad, this study provides a structured and detailed understanding of how these vulnerabilities can compromise Large Language Models (LLMs). Furthermore, it offers targeted mitigation strategies such as input validation, adversarial training, and access controls that can enhance the resilience of LLMs in real-world deployments across sensitive domains. The existing literature does not offer a cohesive and comprehensive classification of prompt-based security threats. While some studies have examined isolated vulnerabilities or introduced preliminary categorizations, there remains a clear need for a holistic framework that captures the full spectrum of prompt injection threats and their implications. To the best of our knowledge, peer-reviewed surveys addressing these threats remain scarce. For instance, [PERSON] et al. [4] acknowledged the importance of developing a taxonomy based on the CIA triad. While this work laid an important foundation by introducing the CIA framework, it lacked depth in analyzing emerging threats and did not fully address their mechanisms and mitigation strategies.