Enhancing the Robustness of Zero-Shot LLMs Against Adversarial Prompts

Zero-shot large language models (LLMs) have proven highly effective in performing a wide range of tasks without the need for task-specific training, making them versatile tools in natural language processing. However, their susceptibility to adversarial prompts—inputs crafted to exploit inherent weaknesses—raises critical concerns about their reliability and safety in real-world applications. This paper focuses on evaluating the robustness of zero-shot LLMs when exposed to adversarial scenarios. A detailed evaluation framework was developed to systematically identify common vulnerabilities in the models' responses. The study explores mitigation techniques such as adversarial training to improve model resilience, refined prompt engineering to guide the models toward desired outcomes, and logical consistency checks to ensure coherent and ethical responses. Experimental findings reveal substantial gaps in robustness, particularly in handling ambiguous, misleading, or harmful prompts. These results underscore the importance of targeted interventions to address these vulnerabilities. The research provides actionable insights into improving zero-shot LLMs by enhancing their robustness and ensuring ethical adherence. These contributions align with the broader goal of creating safe, reliable, and responsible AI systems that can withstand adversarial manipulation while maintaining their high performance across diverse applications.