OpenAI’s API guide covers prompt engineering strategies and tactics for working with large language models.
Tensor Trust
Tensor Trust offers an interactive game about testing AI security and model robustness.

About Tensor Trust
Tensor Trust is the open source code for a prompt injection game that collects data for adversarial machine learning research. The repository contains the web game source code and a data cleaning pipeline, with references to use the data and play the game.
Running a local copy requires manual setup, including an OpenAI API key, Redis, Node.js, a Python 3.10 environment, and database migration. Academic publications that build on the code or data are asked to cite the associated 2023 paper using the included BibTeX entry.
Key features
- Prompt injection attack game for collecting research data
- Web game source code for local hosting
- Data cleaning pipeline for collected game data
- Django models with migration commands for database management
- Admin login page for site administration
- Tailwind workflow for updating stylesheet during development
- Open source repository with game and data pipeline code
- Provides data and game access for research use
- Includes BibTeX citation for academic reuse
- Local setup requires an OpenAI API key and additional software
- Setup involves command-line steps and database migration
Tensor Trust at a glance
- Category
- AI & Machine Learning › AI Learning & Guides
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Windows
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Tensor Trust alternatives
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The Prompt Engineering Guide teaches prompt engineering and provides learning materials about language models.
Play Tensor Trust to explore prompt injection attacks and defenses through interactive challenges.
Test AI hacking skills and explore language model limitations through the Gandalf interactive challenge.
This guide explains prompt techniques for generating art and copy, with templates and style suggestions.
AnimatedLLM uses animations to explain the mechanisms behind large language models.
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