Cookbook for insecure code detection

[mhbuehler]

Summary

This PR adds a cookbook and dataset for vulnerability detection proposed in #1100

Motivation

LLMs like GPT4 have shown proficiency in classifying code as secure or insecure. This notebook demonstrates prompts that improve classification accuracy from 67% to 80%, potentially helping developers with their secure coding practices. For more details, refer to this blog post. This use case demonstrates:

• Using LLMs to identify and/or correct software vulnerabilities
• Experimenting with multiple prompt techniques (zero-shot, few-shot, KNN few-shot) and measuring performance impacts
• Using the OpenAI API to perform binary classification

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For new content

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