Introduction
This repository hosts research aimed at addressing the challenges posed by prompt injection attacks on large language models (LLMs). These attacks involve manipulating the input prompts to generate undesired outputs. In a landscape where AI systems are widely deployed, ensuring their robustness and security has become paramount.
Key Features
- Defense Strategies: The research proposes various defense mechanisms specifically designed to mitigate prompt injection risks.
- Robustness Improvement: Aimed at enhancing the overall robustness of language models against potential attacks.
- Security Focus: Emphasizes the importance of developing models that can maintain user privacy and data security.
Benefits
- Practical Insights: The findings can guide practitioners in developing more secure AI systems.
- Contributions to the Field: Provides valuable contributions towards the advancement of AI security, particularly in mitigating threats linked to prompt manipulation.
Highlights
- Easy Setup: To run the research applications, an API key must be added to
secret.py, and the results can be viewed by executingapplication_test.py. - Collaboration: This project welcomes contributions and feedback to enhance its scope and effectiveness.
