OWASP Generative AI Security Project

Summary

The OWASP Generative AI Security Project is the first comprehensive security framework specifically designed for the unique risks of generative AI systems. Unlike traditional AI security approaches that focus on supervised learning models, this project tackles the complex security challenges of autonomous AI agents, multi-step AI workflows, and the emerging threat landscape of deepfakes and AI-generated content. Built by security practitioners for security practitioners, it provides actionable testing methodologies, adversarial red teaming techniques, and practical guidance for protecting against the top 10 GenAI security risks that traditional cybersecurity frameworks miss.

What Makes This Different from Traditional AI Security

This isn't just another AI ethics framework or generic risk assessment tool. The OWASP GenAI Security Project specifically addresses vulnerabilities that only exist in generative AI systems:

• Autonomous Agent Risks: Traditional AI models make predictions; generative AI agents take actions. This project provides security controls for AI systems that can browse the web, execute code, and interact with external APIs without human oversight.
• Multi-Step Workflow Vulnerabilities: While most security frameworks focus on single-model deployments, this project addresses the compound risks that emerge when multiple AI models work together in complex workflows, where a breach in one step can cascade through the entire system.
• Content Authenticity Challenges: Beyond data poisoning, this framework tackles deepfakes, synthetic content detection, and the unique challenge of securing systems that blur the line between human and AI-generated content.

Core Security Domains Covered

• Data Protection and Leakage Prevention: Specific techniques for preventing training data extraction, membership inference attacks, and inadvertent disclosure of sensitive information through AI outputs.
• Adversarial Attack Mitigation: Red teaming methodologies designed specifically for generative AI, including prompt injection defenses, model inversion protection, and robust evaluation techniques.
• Autonomous System Containment: Security controls for AI agents that can modify their own behavior, including sandboxing techniques, action logging, and fail-safe mechanisms.
• Supply Chain Security: Guidance for securing the unique GenAI supply chain, from foundation model dependencies to third-party plugins and extensions.

Who This Resource Is For

• Security Engineers and Architects implementing GenAI systems in production environments who need concrete security controls beyond generic AI governance principles.
• DevSecOps Teams responsible for securing AI/ML pipelines and need specific testing methodologies for generative AI vulnerabilities that don't exist in traditional software.
• Risk and Compliance Professionals tasked with assessing GenAI deployments who need structured risk taxonomies that map to actual technical vulnerabilities rather than high-level ethical concerns.
• Product Security Teams at companies building GenAI features who need practical guidance for secure development practices, threat modeling, and security testing specific to generative AI capabilities.

Getting Your Hands Dirty: Practical Application

The project provides immediately usable tools rather than theoretical frameworks. You'll find specific test cases for prompt injection attacks, code examples for implementing output filtering, and step-by-step red teaming scenarios you can run against your own systems.

The testing methodologies include actual attack vectors with sample payloads, making this a hands-on security resource. The project maintains an active repository of security test cases that you can integrate into your existing security testing pipeline.

Unlike academic research papers, this project focuses on what security teams can implement today with existing tools and technologies, while also preparing for emerging threats in the rapidly evolving GenAI landscape.