Cookbook for insecure code detection

[mhbuehler]

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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

• [x] I have added a new entry in registry.yaml (and, optionally, in authors.yaml) so that my content renders on the cookbook website.
• [x] I have conducted a self-review of my content based on the contribution guidelines:
  • [x] Relevance: This content is related to building with OpenAI technologies and is useful to others.
  • [x] Uniqueness: I have searched for related examples in the OpenAI Cookbook, and verified that my content offers new insights or unique information compared to existing documentation.
  • [x] Spelling and Grammar: I have checked for spelling or grammatical mistakes.
  • [x] Clarity: I have done a final read-through and verified that my submission is well-organized and easy to understand.
  • [x] Correctness: The information I include is correct and all of my code executes successfully.
  • [x] Completeness: I have explained everything fully, including all necessary references and citations.