Evaluation of Domain-Specific Prompt Engineering Attacks on Large Language Models

The rapid integration of artificial intelligence into critical domains such as healthcare, finance, and legal services has necessitated a closer examination of the robustness and reliability of advanced language models. Adversarial prompt engineering presents a novel and significant method to systematically evaluate and exploit vulnerabilities within these models, highlighting the imperative for enhanced defensive strategies. A comprehensive evaluation was conducted on Claude and Gemini models, employing domain-specific adversarial prompts to test their performance across various sectors. The results indicated significant degradation in accuracy, reliability, and response time under adversarial conditions, revealing context-dependent vulnerabilities that compromise model integrity. Detailed statistical analyses and visualizations illustrated the substantial impact of adversarial inputs, providing robust evidence of the necessity for improved mitigation techniques. Patterns of susceptibility were identified, suggesting the need for tailored defensive approaches for different domains. The study contributes valuable insights into the inherent weaknesses of advanced language models, emphasizing the importance of ongoing research and development to enhance model resilience and ensure their reliable deployment in real-world applications.