OWASP AI Exchange

Summary

The OWASP AI Exchange is a community-driven framework that systematically maps AI attack surfaces and codifies security testing methodologies specifically for AI systems. Unlike traditional cybersecurity frameworks that treat AI as an afterthought, this resource was built from the ground up to address the unique vulnerabilities and attack vectors that emerge in machine learning pipelines, model deployment, and AI system operations. As a 2024 release from the Open Web Application Security Project, it represents the cybersecurity community's consolidated knowledge about AI-specific threats and provides actionable guidance for implementing security controls at enterprise scale.

What makes this different

Traditional security frameworks struggle with AI systems because they don't account for model poisoning, adversarial inputs, data drift, or inference-time attacks. The OWASP AI Exchange fills this gap by creating a taxonomy that's specifically designed for the AI threat landscape. Rather than adapting web application security principles to AI (which often doesn't work), this framework identifies attack vectors that are unique to machine learning systems - like training data manipulation, model extraction attacks, and prompt injection vulnerabilities.

The community-driven approach means it's constantly evolving based on real-world AI security incidents and emerging research, making it more current than static frameworks developed by individual organizations.

Core attack surface mapping

The framework organizes AI vulnerabilities across the entire ML lifecycle:

• Training Phase Threats: Data poisoning, backdoor insertion, supply chain attacks on training datasets, and malicious data labeling that can compromise model integrity from the start.
• Model Development Risks: Model stealing, intellectual property theft, adversarial example generation, and attacks that target the model architecture or parameters during development.
• Deployment Vulnerabilities: Infrastructure attacks targeting AI serving systems, model versioning attacks, and runtime manipulation of inference processes.
• Operational Attack Vectors: Drift-based attacks that exploit model performance degradation, feedback loop manipulation, and attacks on model monitoring systems.

Security testing methodologies

Beyond just identifying threats, the framework provides concrete testing approaches:

• Adversarial testing protocols for evaluating model robustness against malicious inputs
• Data integrity validation methods to detect poisoned or corrupted training data
• Model behavior analysis techniques to identify backdoors or unexpected model responses
• Infrastructure security assessments tailored to AI system architectures
• Automated testing pipelines that can be integrated into MLOps workflows

Who this resource is for

• Security teams at AI-adopting organizations who need to extend their existing security programs to cover AI systems and want concrete guidance on AI-specific vulnerabilities.
• AI/ML engineers and data scientists who are responsible for implementing security controls in their models and need a structured approach to threat modeling for AI systems.
• Risk and compliance professionals who must assess and document AI-related risks for regulatory reporting or internal risk management processes.
• Penetration testers and security researchers who want to develop AI security testing capabilities and need a framework for systematic AI vulnerability assessment.
• Enterprise architects designing AI governance frameworks who need a technical foundation for security requirements and controls.

Getting started with AI Exchange

Begin by using the framework's threat modeling templates to map your specific AI system architecture and identify relevant attack vectors. The framework provides worksheets and checklists that guide you through assessing each component of your AI pipeline.

Next, implement the baseline security testing methodologies that align with your AI system's risk profile. Start with automated tests that can be integrated into your existing CI/CD pipelines, then gradually add more sophisticated adversarial testing capabilities.

The framework also includes risk mitigation playbooks that map specific controls to identified threats, helping you prioritize security investments based on your organization's AI attack surface.