Prompt Injection

Prompt injection attacks exploit the instruction-following capabilities of large language models (LLMs) by subtly embedding malicious instructions within user prompts, causing the model to deviate from its intended function and potentially reveal sensitive information or perform harmful actions. Current research focuses on developing robust testing frameworks (like fuzzing techniques) and defensive strategies, including prompt tuning and task-specific fine-tuning, to mitigate these vulnerabilities. The significance of this research lies in ensuring the secure and reliable deployment of LLMs in real-world applications, particularly those involving sensitive data or critical tasks, by identifying and addressing these emerging security threats.