Large language models (LLMs) are increasingly integrated into our daily lives, from chatbots to content creation. But how resilient are these powerful tools against manipulation? New research explores the robustness of popular LLMs like GPT-3.5-turbo and GPT-4 against two types of adversarial attacks: subtle character-level text attacks and more sophisticated "jailbreak" prompts. Character-level attacks involve introducing minor typos or alterations into the input text, mimicking common human errors. Researchers tested how these small changes affected the LLMs' performance on sentiment analysis tasks using datasets like IMDB movie reviews and Yelp reviews. The results were striking – even tiny errors significantly reduced the accuracy of all models tested. This suggests that LLMs can be surprisingly sensitive to minor textual corruptions, raising concerns about their reliability in real-world scenarios where perfect input isn't always guaranteed. Jailbreak prompts, on the other hand, are deliberately crafted to trick LLMs into bypassing their safety mechanisms and generating responses that violate ethical guidelines. These prompts exploit loopholes in the models' design, potentially leading to the generation of harmful or inappropriate content. The research used a dataset of over 1,400 jailbreak prompts collected from various online sources. While newer models like GPT-4 demonstrated better resilience, detecting over 90% of these attacks, GPT-3.5-turbo identified less than half, revealing a significant vulnerability. This difference highlights the ongoing development and improvement of safety measures in newer LLM iterations. These findings underscore the importance of continuous research into LLM robustness. As these models become more deeply embedded in our lives, ensuring their resilience against manipulation, both subtle and overt, is crucial for maintaining their safety and trustworthiness. Future work should focus on developing more robust training methods and defense mechanisms to protect LLMs from these evolving threats, paving the way for their responsible and beneficial deployment in various applications.