# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 4 Real-World Implications  
The vulnerabilities and attack vectors discussed here have significant real-world implications, particularly in critical domains where Large Language Models (LLMs) are increasingly deployed. Sectors such as healthcare, finance, legal services, public trust and safety, and regulatory compliance are particularly affected, as security breaches in these areas can lead to privacy violations, malicious code generation, misinformation, and operational disruptions. These sectors were selected for analysis due to their substantial reliance on LLMs in essential functions such as automated decision-making, customer support, and data processing. They also manage highly sensitive information, including personal, financial, and legal data, making them prime targets for adversarial prompt  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Attack Type** & **C** & **I** & **A** \\ \hline Data Extraction Attack & ✓ & \(\times\) & \(\times\) \\ Model Inversion Attack & ✓ & \(\times\) & \(\times\) \\ Prompt Stealing Attack & ✓ & \(\times\) & \(\times\) \\ Instruction Injection & ✓ & ✓ & \(\times\) \\ Toxic Prompting & \(\times\) & ✓ & \(\times\) \\ Semantic Manipulation & \(\times\) & ✓ & \(\times\) \\ Deceptive Fairness Attack & ✓ & ✓ & \(\times\) \\ Prompt Denial of Service & \(\times\) & \(\times\) & ✓ \\ Context Flooding Attack & \(\times\) & \(\times\) & ✓ \\ Output Degradation Attack & \(\times\) & ✓ & ✓ \\ Backdoor Trigger Attack & \(\times\) & ✓ & ✓ \\ Universal Adversarial Example Attack & \(\times\) & ✓ & ✓ \\ Fairness Evasion Attack & ✓ & ✓ & \(\times\) \\ Toxicity Injection & \(\times\) & ✓ & \(\times\) \\ Context Injection & \(\times\) & ✓ & ✓ \\ \hline \hline \end{tabular} Analysis and Implications  
To further illustrate the impact of these attacks, Table 5 outlines their focus, examples, and implications. This detailed breakdown highlights how each dimension of the CIA triad is compromised by specific attack types.  
\end{table}
Table 4: Mapping prompt attacks to the CIA triad. ✓ indicates that the attack impacts the respective CIA dimension, while \(\times\) indicates no significant impact.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**CIA Dimension** & **Attack Focus** & **Examples** & **Implications** \\ \hline \multirow{4}{*}{**Confidentiality**} & \multirow{4}{*}{Extract sensitive or proprietary information} & Data Extraction: Retrieving personal data or trade secrets. & Breach of privacy and data protection laws, unauthorized access to confidential information, impacting trust. \\  & & & Reconstructing sensitive inputs. & \\ \hline \multirow{4}{*}{**Integrity**} & Manipulate outputs to generate biased, false, or harmful content & Toxic Prompting: Inducing offensive or harmful content. Instruction Injection: Overriding safety measures. & Dissemination of misinformation, propagation of harmful stereotypes or narratives, erosion of user trust. \\  & & & Prompt-Based \\ \multirow{4}{*}{**Availability**} & Disrupt system usability and responsiveness through overwhelming inputs & & Denial-of-Service: Overloading the model. Context Flooding: Filling the context window with irrelevant data. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis and implications of prompt attacks.  
attacks. Moreover, vulnerabilities in these domains can result in far-reaching consequences, including economic instability, compromised legal proceedings, diminished public trust, and noncompliance with regulatory frameworks.