What it does
We used a fuzzing approach to create a system that iteratively generates adversarial prompts to fool LLMs. We created a dataset of hundreds of adversarial prompts and scores on Mistral LLM responses.
How we built it
- NextJS Frontend
- ShadCN, RadixUI, and Tailwind CSS components
- Python Flask Backend
- Mistral Chat API
- Prompt Generation
- Testing
- LLM as Judge
- Mistral Embedding API
- MongoDB Atlas
- Vector Search
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