This study went on a comprehensive evaluation of four prominent Large Language Models (LLMs)-Google Gemini, Mistral 8x7B, ChatGPT-4, and Microsoft Phi-1.5-to assess their robustness and reliability under a variety of adversarial conditions. Utilizing the Microsoft PromptBench dataset, the research investigates each model's performance against syntactic manipulations, semantic alterations, and contextually misleading cues. The findings reveal notable differences in model resilience, highlighting the distinct strengths and weaknesses of each LLM in responding to adversarial challenges. Comparative analysis underscores the necessity for multifaceted evaluation approaches to enhance model resilience, suggesting future research directions involving the augmentation of training datasets with adversarial examples and the exploration of advanced natural language understanding algorithms. This study contributes to the ongoing discourse in LLM research by providing insights into model vulnerabilities and advocating for comprehensive strategies to bolster LLM robustness against the evolving landscape of adversarial threats.

This study presents a comprehensive evaluation of the cybersecurity robustness of five leading Large Language Models (LLMs)-ChatGPT-4, Google Gemini, Anthropic Claude, Meta Llama, and Mistral 8x7B-against adversarial prompts using the PromptBench benchmark. Through a dual approach of quantitative and qualitative analysis, the research explores each model's performance, resilience, and vulnerabilities. Quantitative metrics such as accuracy, precision, recall, and F1 scores offer a statistical comparison across models, while qualitative insights reveal distinct patterns of response and susceptibility to various adversarial strategies. The findings highlight significant variations in model robustness, underlining the importance of a complex approach to enhancing LLM security. This study not only sheds light on current limitations but also emphasizes the need for advancing evaluation methodologies and model development practices to mitigate potential threats and ensure the safe deployment of LLMs in sensitive and critical applications.