gpu-security
Safeguard articles tagged "gpu-security" — guides, analysis, and best practices for software supply chain and application security.
4 articles
Securing Containerized AI Workloads: Base Images, GPU Drivers, and Runtime Policy
A CVSS 9.0 flaw in NVIDIA's Container Toolkit let any GPU container escape to the host — and its first patch didn't fully close it. Here's how to defend AI infrastructure.
AI Chip Architecture: A Security Guide for Accelerated Compute
AI chip architecture shapes the security surface of accelerated compute. Here is how GPUs, TPUs, and NPUs are built and where the real risks live.
AI Accelerators: A Security Guide to the Hardware Running Your Models
AI accelerators are the specialized chips that make model training and inference fast, and they bring their own attack surface: memory leakage, driver stacks, and firmware you did not write. Here is what to secure.
AI Chips Explained: What They Are and How to Secure Them
AI chips are specialized processors built to run matrix math at scale, and the way you provision, share, and supply-chain-source them creates security risk most teams never model.
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