AI Goat is an open-source project built by security practitioners who believe the best way to learn AI security is by attacking real systems in a safe, local environment. Every contribution makes the platform stronger for everyone.
The people who started it all
Application security professional with deep expertise in AI/ML security, LLM red teaming, and building security tooling. Farooq leads the architecture, vision, and development of AI Goat — driven by the belief that hands-on practice is the most effective way to learn security.
Security researcher and engineer focused on AI safety, adversarial machine learning, and building resilient AI systems. Nalinikanth shapes the attack labs, CTF challenge architecture, and defense mechanisms that make AI Goat an authentic learning environment.
A GitHub star costs nothing and means everything. It helps other security practitioners discover the project.
How you can help build AI Goat
Every merged contribution earns you a permanent spot on this page. Pick an area that matches your expertise.
Design and implement vulnerability labs for emerging LLM attack vectors.
Build guardrails, filters, and defense strategies across all three security levels.
Write tutorials, walkthroughs, and educational content to help others learn.
Create new CTF-style challenges with dynamic flags and exploit-gated evaluation.
Test attack scenarios, report bugs, and contribute your security research findings.
Review pull requests, improve code quality, and ensure security of the platform itself.
2 Contributors
Everyone who has contributed to AI Goat on GitHub. Submit a pull request to join this wall.
AI Goat
