jiep/offensive-ai-compilation

What it solves

This project provides a curated, centralized collection of resources focused on "Offensive AI." It helps researchers, security professionals, and developers understand how AI models can be exploited, how to assess their vulnerabilities, and what defensive measures exist to protect them.

How it works

The repository acts as a structured knowledge base organized by attack vectors and application areas. It categorizes adversarial machine learning into four primary attack types—extraction, inversion, poisoning, and evasion—while providing links to academic papers, tools, and defensive strategies for each. It also covers practical offensive use cases like pentesting, malware, and generative AI manipulation.

Who it’s for

This compilation is intended for security researchers, AI engineers, and academics who need to study the threat landscape of machine learning systems, evaluate model robustness, or implement security countermeasures against adversarial attacks.

Highlights

• Adversarial ML Taxonomy: Detailed breakdown of extraction, inversion, poisoning, and evasion attacks.
• Defensive Strategies: Curated lists of countermeasures, including differential privacy, adversarial training, and data sanitization.
• Tooling Resources: References to established frameworks like ART (Adversarial Robustness Toolbox) and Cleverhans.
• Generative AI Security: Specific sections on detecting and manipulating audio, image, video, and text models.
• Academic Foundation: Extensive collection of peer-reviewed papers and research links for deep-dive study.