model-security
Safeguard articles tagged "model-security" — guides, analysis, and best practices for software supply chain and application security.
20 articles
What Is an AI Accelerator, and What Are Its Security Risks?
An AI accelerator is hardware built to speed up machine learning math. Once you offload models onto one, the security work shifts to the software and data around it.
What Is an AI Factory, and How Do You Secure One?
An AI factory is the industrialized pipeline that turns data and compute into deployed models at scale. Treating it like a factory means securing every stage, not just the model.
AI Supply Chain Attacks: Emerging Threats in Model and Data Pipelines
As organizations adopt AI at scale, the AI/ML supply chain is becoming a new attack surface. From poisoned models to compromised training data, the threats are real and growing.
What is Adversarial Machine Learning
Adversarial machine learning exploits model decision boundaries via evasion, poisoning, extraction, and inference attacks -- here's how it works and how to defend against it.
Open Source AI Model Security: The Emerging Threat Landscape
As open source AI models proliferate, their security implications extend far beyond traditional software vulnerabilities. Model poisoning, supply chain tampering, and unsafe deserialization create new attack surfaces.
AI Models in Your Supply Chain: The Security Risks Nobody Talks About
AI/ML models are the new open source libraries. Here's why your supply chain security strategy needs to account for model provenance, poisoning, and compliance.
AI Model Supply Chain Risks: Hugging Face and the New Attack Surface
As organizations download pre-trained models from Hugging Face and other model hubs, the AI supply chain introduces risks that traditional software security tools don't address.
Vulnerability Scanning for AI Models: A New Frontier
AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.
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