Skip to content

Data Gathering Methodology

Jump to bottom Edit New page
emmanuelgjr edited this page Sep 26, 2023 · 21 revisions

Introduction to Data Gathering Methodology for OWASP TOP 10 for LLM AI Applications

Welcome to our dedicated GitHub wiki for understanding and advancing the data-gathering methodology pertaining to OWASP's Top 10 for LLM AI Applications. As technology continues to evolve at an unprecedented pace, particularly in the domains of artificial intelligence and deep learning, securing these systems is of paramount importance. This wiki serves as a central repository for methodologies, strategies, and tools associated with understanding and prioritizing vulnerabilities in LLMs based on real-world data.

Why this Wiki?

  1. Centralized Knowledge Base: With the multifaceted nature of LLM vulnerabilities, having a one-stop solution where developers, researchers, and security experts can find and contribute to the most recent and relevant methodologies is invaluable.

  2. Collaborative Environment: GitHub offers an interactive platform where community members can collaborate, providing insights, updates, and refinements to the existing methodology.

  3. Transparency & Open Source Spirit: In line with the ethos of OWASP and the open-source community, this wiki promotes transparency in the data-gathering process, ensuring everyone has access to the best practices in vulnerability assessment.

  4. Addressing the Dynamic Nature of Threats: The field of AI security is nascent but growing rapidly. This wiki will act as a live document, continuously evolving to capture the latest threats and vulnerabilities.

What to Expect?

Throughout this wiki, you'll find:

  • Detailed steps and guidelines for data collection related to OWASP vulnerabilities in LLMs.
  • Tools, scripts, and code snippets to aid the data-gathering process.
  • Expert contributions, reviews, and insights on refining the methodology.
  • A section dedicated to ethical considerations, ensuring data is gathered and used responsibly.
  • Community-driven surveys, discussions, and feedback mechanisms.

Whether you're a seasoned security expert, a researcher in AI, or just someone keen on understanding the landscape of LLM vulnerabilities, this wiki is for you. Dive in, explore, contribute, and let's work together to make our AI systems more secure!

Our Slack channel is #team-llm-datagathering-methodology

Data Gathering Methodology ROADMAP

Click the "Edit" button in the top-right corner to make changes/additions. Don't clone it. Just edit in place.

Add a custom sidebar
Clone this wiki locally