Dumpster diving S3

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Dumpster diving S3

This particular story is a favorite of mine. It started way back, one tired night...

(left intentionally empty. check out one of the other articles. this one is special)



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Google Cloud: Container Registry will be replaced by Artifact Registry

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Spoiler Alert: (you've got some time) Container Registry will be replaced by Artifact Registry. Please upgrade your projects to Artifact Registry before March 18, 2025. On March 18, 2025 , Container Registry will be replaced by Artifact Registry . We understand this change may affect your production workloads, so we've put together resources to make this transition as smooth as possible for you. What do you need to know? To retain access to your container images, please take note of these critical dates and actions you need to take related to the Container Registry shutdown: March 18, 2025: You will no longer be able to push new images to Container Registry. April 22, 2025: You will lose access to existing images in Container Registry. To maintain access, you must copy them to the Artifact Registry before this date. May 22, 2025: All requests to the gcr.io domain will be handled exclusively by Artifact Registry. Ensure the Artifact Registry...
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AI in Today's Cybersecurity Landscape

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AI in Today’s Cybersecurity Landscape Top Security Concerns with AI in Today’s Cybersecurity Landscape Integrating Artificial Intelligence (AI) into various sectors has been a game-changer, offering unprecedented advancements and efficiencies. However, this integration has not come without its cybersecurity risks. AI systems are becoming an attractive target for cybercriminals, and their exploitation can lead to significant data breaches, privacy violations, and operational disruptions. Here, we explore the top security concerns with AI, provide examples of recent cyber security attacks, and outline best practices users can adopt to mitigate these risks. AI Security Concerns 1. Data Poisoning:  AI systems learn from data. If attackers manage to introduce malicious data into the training set, they can skew the AI’s behavior. An example is manipulating an AI’s algorithm in autonomous vehicles to misinterpret stop signs as yield signs by slightly altering the image data the AI trains ...
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Meet Kaniko

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So, what is kaniko? Kaniko is a tool to build container images from a Dockerfile inside a container or Kubernetes cluster. Another great callout for me is that my CI provider, GitLab makes it super easy to implement in my pipelines. Kaniko doesn't depend on a Docker daemon and executes each command within a Dockerfile entirely in userspace. This enables building container images in environments that can't easily or securely run a Docker daemon, such as a standard Kubernetes cluster. How does Kaniko work? The kaniko executor image is responsible for building an image from a Dockerfile and pushing it to a registry. Within the executor image, we extract the filesystem of the base image (the FROM image in the Dockerfile). We then execute the commands in the Dockerfile, snapshotting the filesystem in userspace after each one. After each command, we append a layer of changed files to the base image (if there are any) and update image metadata. Highly recommend visiting their  repo  ...
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