A federal appeals court handed GitHub, Microsoft, and OpenAI an important win in the first major appellate ruling over how AI coding tools can use open-source code. As we all know, all the AI code-generating programs learned their lessons largely from open-source code. So a group of anonymous open-source plaintiffs argued that GitHub Copilot and OpenAI Codex were built using code from public GitHub repositories, including open-source licensed code. They argued the AI tools had generated code without the author attribution, copyright notices, and license terms that came with the original work. Now, the Ninth Circuit has returned a verdict in Doe vs. GitHub siding with Big AI. Ouch! But it may not be as troublesome as it first appears. The Ninth Circuit’s September 16 decision turns on a technical but consequential distinction: AI-generated code that lacks author names, copyright notices, and license information is not necessarily the same as code from which that information has been...
Your team probably spends considerable effort securing your application. WAFs, rate limiting, input validation, penetration testing. Then code passes through a CI/CD pipeline that runs with privileged credentials, executes arbitrary code from third-party actions, has broad network access to internal systems, and logs secrets in plain text if someone makes one configuration mistake. The pipeline that builds and deploys your secure application is itself the least secure system in your infrastructure. Think about what a CI/CD pipeline actually is from an attacker’s perspective. It is a system that automatically executes code, often with administrative access to your cloud accounts, container registries, databases, and production environments. It pulls dependencies from the public internet. It runs plugins and actions written by strangers. It often has access to every secret needed to deploy to every environment. If you designed a system specifically to be an attractive target for s...