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GitHub Actions – Force cancel workflows

Actions customers will now be able to clear stuck workflows by forcing a cancel request from the REST API. This is a new feature and the existing endpoint to cancel a workflow run will remain unchanged.

Sometimes an Actions workflow can become stuck in a state that will not respond to a cancel request. This could block other workflows from executing and would often require customers to contact GitHub Support to resolve the issue. Going forward, customers can invoke force-cancel from the REST API, which will bypass conditions that would otherwise cause the workflow execution to continue. Customers should still only use force-cancel if the workflow fails to respond to POST /repos/{owner}/{repo}/actions/runs/{run_id}/cancel.

For more details see the GitHub Actions workflow runs REST API documentation.

For questions, visit the GitHub Actions community.

To see what's next for Actions, visit our public roadmap.

Now generally available, GitHub Enterprise Cloud customers with enterprise managed users (EMU) can integrate with Ping Federate as a formally supported SSO and SCIM identity provider. To get started, download the Ping Federate "GitHub EMU Connector 1.0" from the add-ons tab on the download page, under the "SaaS Connectors" heading. Add the connector to your Ping Federate installation and consult the Ping Federate documentation in addition to GitHub's SAML SSO and SCIM documentation for configuration.

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The "GitHub EMU Connector" is maintained and supported by our partner, Ping Identity. Ping additionally maintains their own release notes for this connector.

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With CodeQL model packs for Java, users can improve their code scanning results by ensuring that any custom Java libraries and frameworks used by their codebase are recognised by CodeQL.

The out-of-the-box CodeQL threat models provide great coverage for identifying large numbers of potential vulnerabilities in GitHub repositories using code scanning. We are continually working to improve CodeQL's ability to recognize and track potential sources of untrusted data to potentially-vulnerable locations ('sinks'). To do that, we keep a close eye on the most widely-used open-source libraries and frameworks. That way, CodeQL can recognize untrusted data that enters an application through, for example, commonly-used web frameworks. We are even using advances in AI to boost our threat modeling efforts and help developers write even more secure code.

There will always be cases which are not covered by CodeQL's standard threat models, such as custom-built or inner-sourced frameworks and libraries. Using CodeQL's new model pack functionality for Java (beta), security teams and security-conscious developers can create custom models that help CodeQL detect and flag additional security vulnerabilities. These custom model packs work seamlessly in GitHub code scanning, which means developers get the most relevant code scanning alerts during their day-to-day work.

CodeQL model packs are part of the CodeQL package management ecosystem. The packs contain structured data which describe whether a method within a library is a taint source, sink, or propagator (also known as a flow summary). You can create CodeQL model packs for Java using the CodeQL model editor, a new feature in the CodeQL extension for VS Code. The CodeQL model editor includes support for:

  • identifying methods in your codebase that aren't recognised by the standard CodeQL analysis
  • interactively classifying those methods as a source, sink, or summary
  • automatically generating a CodeQL model pack that can be easily added to code scanning.

For more information about using CodeQL model packs in code scanning, see:

For more information about using the CodeQL model editor, see Using the CodeQL model editor.

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