# Acknowledgments

This repository draws from multiple sources, including academic research, community efforts, and existing projects that document prompt injection techniques. We would like to acknowledge the following:

## Projects

- [Garak](https://github.com/leondz/garak): A tool for LLM vulnerability scanning and penetration testing
- [Denzel-Crocker-Hunting-For-Fairly-Odd-Prompts](https://github.com/rabbidave/Denzel-Crocker-Hunting-For-Fairly-Odd-Prompts): Collection of jailbreak templates
- [PromptInject](https://github.com/agencyenterprise/promptinject): Framework for testing LLM prompts against adversarial inputs

## Research

We've incorporated insights from various research papers and publications in the field of LLM security:

- Various academic papers on prompt injection techniques and defenses
- Industry security reports documenting LLM vulnerabilities
- Security advisories from AI system providers

## Community Contributors

This repository has benefited from the contributions and feedback of many individuals in the AI security community. We appreciate everyone who has contributed examples, suggested improvements, or helped classify and document techniques.

## Citing This Work

If you use this resource in your research or projects, please cite:

```
@misc{prompt-injections,
  author = {Astley, William},
  title = {Prompt Injection Techniques Repository},
  year = {2025},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/pr1m8/prompt-injections}}
}
```