AI/ML Penetration Testing
AI innovation and the fast adoption of ML systems into production is happening whether we like it or not. It’s transforming the way we work, and we’ve only scratched the surface of its potential. As adoption skyrockets, organizations must understand the unique threat landscape that accompanies AI/ML systems, identify areas of weakness, and build more resilient models. We want to be your security partner through it all – from ideation to training to implementation.
A Comprehensive Approach to Machine Learning Security
There is no silver bullet for AI/ML security. But a penetration test is a great opportunity to identify, understand, and mitigate risks and improve overall resiliency to attacks. Chat with our team to understand which of our AI/ML security assessments is right for you or collaborate on a customized approach to meet your unique offensive security needs.
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Machine Learning Security AssessmentA comprehensive assessment designed to evaluate ML models, including Large Language Models (LLMs), against adversarial attacks, identify vulnerabilities, and provide actionable recommendations to ensure the overall safety of the model, its components, and their interactions with the surrounding environment.
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Infrastructure Security AssessmentTest the surrounding infrastructure around your model. This assessment covers network security, cloud security, API security, and more, ensuring that your organization’s deployment adheres to defense in depth security policies and mitigates potential risks.
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Web Application Penetration TestingTest the security and reliability of web applications utilizing LLMs and other machine learning integrations. Leveraging sophisticated manual processes and automated tools, we identify vulnerabilities and risks specific to LLM-integrated functionality, providing actionable recommendations to enhance security and safeguard sensitive data.
Rooted in Adversarial Machine Learning
Our AI/ML Pentesting methodology is rooted in adversarial machine learning, the study of adversarial attacks on ML and corresponding defenses. With this foundational research, our offensive security experts have the knowledge to better understand and mitigate vulnerabilities within ML models by putting them to the test against real-world attack techniques.
Team NetSPI is here to help you identify, analyze, and mitigate the risks associated with adversarial attacks on your machine learning systems. Here’s a glimpse into what an assessment looks like:
We won’t test your models in a silo. Our team collaborates closely with you to contextualize ML with your existing technology stack (cloud, web, applications). Comprehensive testing tailored to your use cases.
We’ll assess the security of your existing pipeline and guide you to implement best practices for secure feature engineering, preprocessing techniques, and model training.
Through a combination of attack simulations, pentesting, and advanced evaluation techniques, measure how your defenses perform against major adversarial attacks such as evasion, poisoning, inference, availability, and extraction.
Our experts provide comprehensive reports and recommendations for remediation and improving defense mechanisms, all delivered via NetSPI’s PTaaS platform. Make informed decisions to enhance your AI/ML security posture.
AI/ML Pentesting Resources
Delivered in our
PTaaS Platform
Web application pentesting engagements are managed and delivered through Resolve™, NetSPI’s vulnerability management and orchestration platform. Resolve™ elevates your vulnerability management and pentesting program.
