Large Language Models (LLMs) like ChatGPT have become incredibly popular, capable of writing stories, answering questions, and even generating code. But behind their impressive abilities lurk significant risks. A new research survey reveals a range of potential dangers from widespread LLM use, impacting everything from personal privacy to global economics. One key issue is privacy. LLMs are trained on massive datasets, and research shows they sometimes memorize parts of this data, potentially exposing sensitive user information. Think of it like an AI accidentally reciting lines from its training data, which could include private conversations or confidential documents. Researchers point to models like GPT-Neo and even Google’s Bard as examples where private data leakage is a real concern. Security is another major vulnerability. LLMs can be tricked with malicious prompts, generating harmful content or even malware. They are also susceptible to adversarial attacks, where hackers exploit weaknesses in their design to steal information or manipulate their behavior. Even seemingly benign features like sentence embeddings can be reverse-engineered to expose private data. Beyond individual risks, the survey also highlights broader societal concerns. LLMs can perpetuate and amplify biases present in their training data, leading to unfair or discriminatory outcomes. Their massive energy consumption poses a significant environmental threat, contributing to CO2 emissions. Furthermore, LLMs are disrupting the labor market, with the potential to automate many existing jobs. The research identifies the root causes of these problems, pointing to factors like the complexity of LLM architectures, lack of public awareness, and the rapid evolution of threats. It also offers potential solutions, such as robust model development, privacy-preserving techniques, and stricter regulatory compliance. This survey serves as a wake-up call, reminding us that the rapid advancement of LLMs needs to be met with careful consideration of their potential risks. The future of AI depends on finding a balance between innovation and responsible development, ensuring that these powerful tools are used for good, not harm.