Large language models (LLMs) have become incredibly powerful tools, capable of generating human-like text, translating languages, and even writing different kinds of creative content. But how robust are these models when faced with adversarial attacks—subtle manipulations designed to trick them? A new study probes the vulnerabilities of open-source LLMs like Llama, OPT, and T5, revealing some intriguing insights. Researchers put these models through their paces, testing their performance on various text classification tasks after subjecting them to carefully crafted adversarial perturbations. The results show that while model size does play a role in robustness, the relationship isn't straightforward. Bigger isn't always better, as even the largest models showed susceptibility to these attacks. Interestingly, popular fine-tuning techniques like LoRA and quantization didn't significantly impact the models' ability to withstand adversarial attacks. This suggests that while these methods are great for optimizing performance, they don't necessarily make models more resilient. The study also highlights the importance of model architecture. Models with a classification head, designed for simpler output, were found to be more vulnerable than those without. This could be because the simpler structure makes it easier for attackers to identify and exploit weaknesses. These findings underscore the need for ongoing research into LLM robustness. As LLMs become increasingly integrated into our lives, ensuring they can withstand adversarial attacks is crucial for building trust and reliability. Future research could explore the impact of newer techniques like reinforcement learning from human feedback (RLHF) and model parallelism on robustness. Developing more sophisticated adversarial attacks will also be key to gaining a deeper understanding of LLM strengths and weaknesses, paving the way for more secure and trustworthy AI systems.