Prompt Extraction Benchmark

Prompt extraction benchmarks evaluate the vulnerability of large language models (LLMs) integrated into applications to attacks aiming to steal their internal prompts or instructions. Research focuses on developing robust benchmarks that comprehensively assess model susceptibility under various attack scenarios and the effectiveness of different defense mechanisms, often employing machine learning techniques for both attack and defense strategies. These benchmarks are crucial for improving the security and privacy of LLM-powered applications, particularly those handling sensitive data, by identifying weaknesses and guiding the development of more resilient systems. The ultimate goal is to ensure responsible and secure deployment of LLMs in diverse real-world applications.