Practical Jupyter notebooks from Andrew Ng and Giskard team's "Red Teaming LLM Applications" course on DeepLearning.AI.

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Adversarial LLM red-teaming with Giskard: automated vulnerability scans of DeepSeek-R1, GPT-4o-mini & Llama 3.2 (prompt injection, hallucination, harmful output) plus tabular ML scanning, with per-model HTML reports

🎓 As part of a course project with a classmate, we explored prompt injection attacks by directly interacting with large language models (LLMs) to understand their vulnerabilities. We also presented how the open-source tool Giskard can help secure these models through automated testing and vulnerability detection.

Develop a system to generate diverse types of subjective questions from PDFs and automate the grading of answers.

Different approaches to evaluate RAG !!!

A RAG system to answer questions about flights

PRML pre-registration for Giskard scenario results: commit an eval claim to a SHA-256 before the run, then verify the result against it.

Multi-agent LLM-driven SOC pipeline (n8n + Ollama), adversarially red-teamed against the CSA Agentic AI Red Teaming Guide.

The complete pipeline for a fine-tuning of a classifier model.