AI on

The Guardener

Mon, 30 Mar 2026 00:00:00 +0000

The Guardener migrates your Dockerfiles to use Chainguard Containers. It uses AI to iteratively convert instructions, build images, compare results, and fix issues until the Dockerfile works as expected.

You interact with it through chainctl agent dockerfile commands. The AI runs server-side and scans your workspace to perform its analysis. Docker builds and file access remain local to your machine, and only the data necessary for analysis is processed.

Note: The Guardener is in beta.

Beyond Zero: Eliminating Vulnerabilities in PyTorch Container Images (PyTorch 2024)

Sat, 07 Sep 2024 01:21:01 +0000

Recording of Beyond Zero: Eliminating Vulnerabilities in PyTorch Container Images presented by Dan Fernandez, Srishti Hegde, and Patrick Smyth at PyTorch 2024

Session Description

Container images are increasingly the future of production applications at scale, providing reproducibility, robustness, and transparency. As PyTorch images get deployed to production, however, security becomes a major concern. PyTorch has a large attack surface, and building secure PyTorch images can be a challenge. Currently, the official PyTorch runtime container image has 1 CVE (known vulnerabilities) rated critical and 5 CVEs rated high. Improving this situation could secure many deployments that incorporate PyTorch for cloud-based inference or training. In this fast-paced session, we took a deep dive on the official PyTorch image from a vulnerability mitigation perspective, looking hard at included packages, executables, and active CVEs. We identify low-hanging fruit for increasing security, including stripping bloat and building fresh. We also talk about the next level of security practiced in Chainguard’s PyTorch image builds, such as including SBOMs and going distroless. Finally, we consider emerging tools and approaches for analyzing AI artifacts such as models and how these systems can benefit PyTorch in production.

Getting Started with the PyTorch Chainguard Container

Thu, 25 Apr 2024 08:00:00 +0200

Chainguard’s PyTorch container image provides a security-hardened foundation for deep learning workloads with significantly fewer vulnerabilities than traditional PyTorch containers. Built with PyTorch and CUDA support for GPU acceleration, this minimal image maintains full deep learning capabilities while dramatically reducing attack surface. This guide demonstrates fine-tuning models, secure inference deployment, and compares the enhanced security posture to official PyTorch images.

What is Deep Learning?

Deep learning is a subset of machine learning that leverages a flexible computational architecture, the neural network, to address a wide variety of tasks. Neural networks emulate the structure of the brain and consist of interconnected nodes (neurons) that each contain an associated weight and threshold. In concert with an activation function, these values determine whether data is propagated within the network, producing an output layer corresponding to a classification, regression, or other result.

AI with Hardened Container Images

Thu, 24 Jul 2025 17:00:00 +0000

The July 2025 Learning Lab with Patrick Smyth covers AI with Hardened Container Images. In this session, learn how to secure AI workloads by reducing vulnerabilities in container images by over 90%. Patrick demonstrates hands-on techniques for training an animal detection model using PyTorch with hardened container images, creating minimal and secure deployments, and running AI frameworks with zero CVEs.

Sections

0:00 Introduction and updates
2:02 Preparation: Docker pull instructions for demo
3:39 Chainguard! Who are we?
4:34 CVE system fundamentals
6:48 “Boss assigned me to fix Ubuntu” problem
7:41 Introduction to Chainguard Containers
8:54 Zero CVE containers: Real results and comparisons
11:10 How we achieve zero CVEs: Minimal, Fresh, Advisory, Patch
13:24 AI container challenges: Size and complexity
14:59 PyTorch container analysis: CVEs, packages, and executables
16:21 Demo introduction: Image classification with PyTorch
17:59 Demo walkthrough and repository overview
19:28 Demo: Running the training command
22:01 Demo: Downloading test image and running inference
23:20 Recent developments in Chainguard AI containers
25:09 Other AI containers: TensorFlow, KServe, Triton backends
26:46 Q&A
35:18 Chainguard AI course and additional resources

Demo

In the demo, Patrick trains and runs inference on an image classification model using PyTorch and Chainguard’s hardened container image. The model classifies images of octopuses, whales, and penguins, demonstrating how to work with AI workloads securely.