OpenSec Atlas

graph-adversarial-learning-literature: An Open Source Documentation for AI & Machine Learning Security | OpenSec Atlas

AI & Machine Learning Security

Documentation

graph-adversarial-learning-literature

A curated and continuously updated list of adversarial attack and defense research papers focused on graph-structured data.

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About This Tool

A curated list of adversarial attacks and defenses papers on graph-structured data.

Primary Use Case

This repository serves as a comprehensive reference for researchers, practitioners, and students interested in adversarial machine learning on graphs, providing easy access to the latest and most relevant academic papers. It is ideal for those conducting literature reviews, developing new attack or defense methods, or seeking to understand the current state of graph adversarial learning.

Key Features

Curated and regularly updated list of adversarial attack and defense papers on graph data
Papers sorted by upload date for easy access to recent research
Categorization of papers by year, type (attack/defense), target task, and model
Includes links to both papers and associated code repositories when available
Serves as a complement to a comprehensive survey paper on the topic
Searchable by keywords such as conference, task, model, or method
Welcomes community contributions via pull requests

Insights & Recommendations

This repository is not a software tool but a literature resource; users should cite the referenced survey paper if they find the repository helpful. Contributions should follow the specified format for consistency.

Smart Usage Notes

Leverage this repository to stay current on emerging adversarial ML attack vectors targeting graph data.Incorporate findings into red team exercises simulating advanced AI-driven attacks on graph-based systems.Use curated defenses as a knowledge base for blue team to develop robust graph ML model protections.Facilitate purple team collaboration by integrating latest research to improve detection and response strategies.Automate literature updates to feed threat intelligence platforms focused on AI/ML security risks.