CaMeL Security Demonstration – Defending Against (most) Prompt Injections by Design

An interactive application that visualizes and demonstrates Google’s CaMeL (Capabilities for Machine Learning) security approach for defending against prompt injections in LLM agents.

Link to original paper: https://arxiv.org/pdf/2503.18813

All credit to the original researchers

“`

title={Defeating Prompt Injections by Design},
author={Edoardo Debenedetti and Ilia Shumailov and Tianqi Fan and Jamie Hayes and Nicholas Carlini and Daniel Fabian and Christoph Kern and Chongyang Shi and Andreas Terzis and Florian Tramèr},
year={2025},
eprint={2503.18813},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2503.18813},
}
“`

An interactive application that visualizes and demonstrates Google’s CaMeL (Capabilities for Machine Learning) security approach for defending against prompt injections in LLM agents.
Link to original paper: https://arxiv.org/pdf/2503.18813All credit to the original researcherstitle={Defeating Prompt Injections by Design}, author={Edoardo Debenedetti and Ilia Shumailov and Tianqi Fan and Jamie Hayes and Nicholas Carlini and Daniel Fabian and Christoph Kern and Chongyang Shi and Andreas Terzis and Florian Tramèr}, year={2025}, eprint={2503.18813}, archivePrefix={arXiv}, primaryClass={cs.CR}, url={https://arxiv.org/abs/2503.18813}, }

      title={Defeating Prompt Injections by Design}, 
      author={Edoardo Debenedetti and Ilia Shumailov and Tianqi Fan and Jamie Hayes and Nicholas Carlini and Daniel Fabian and Christoph Kern and Chongyang Shi and Andreas Terzis and Florian Tramèr},
      year={2025},
      eprint={2503.18813},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2503.18813}, 
}