The Colossal Defense: Security Challenges of Large Language Models

Large Language Models (LLMs) have become increasingly popular because of their strong capabilities and abilities in multiple fields, such as text generation, language translation, image, video, audio, and other forms of output generation. The ability of the model to analyze a complex input prompt and provide relevant and simplified responses, depending on its usage, elevates its desirability. LLM not only has a transformative effect on the interaction between humans and models and the usage of the models in different situations but also has a significant drawback. Although the main benefits of LLM are apparent, there is a potential landmine of security threats accompanying its existence. Exposure to artificial intelligence increases the security risk throughout its design, development, infrastructure, and incorporation. Threat actors can exploit existing vulnerabilities to control models and execute uncontrollable and divisive actions. This study thoroughly examines the fundamental security problems of prompt injection, data poisoning, and sensitive information extraction. The attacks and defenses arising from these issues related to LLM are summarized. Finally, we suggest future research directions to secure LLMs.