security-and-compliance

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GitHub secret scanning protects users by searching repositories for known types of secrets such as tokens and private keys. By identifying and flagging these secrets, our scans help prevent data leaks and fraud.

We have partnered with Canva to scan for their tokens to help secure our mutual users in public repositories. Canva tokens enable users to perform authentication for their Canva Connect API integrations. GitHub will forward any exposed tokens found in public repositories to Canva, who will then rotate the token and notify the user about the leaked token. Read more information about Canva tokens.

GitHub Advanced Security customers can also scan for and block Canva tokens in their private repositories.

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Use CodeQL threat model settings for Java (beta) to adapt CodeQL's code scanning analysis to detect the most relevant security vulnerabilities in your code.

No two codebases are the same and each is subject to different security risks and threats. Such risks and threats can be captured in a codebase's threat model which, in turn, depends on how the code has been designed and will be deployed. To understand the threat model you need to know what type of data is untrusted and poses a threat to the codebase. Additonally, you need to know how that unstrusted (or tainted) data interacts with the application. For example, one codebase might only consider data from remote network requests to be untrusted, whereas another might also consider data from local files to be tainted.

CodeQL can perform security analysis on all such codebases, but it needs to have the right context. It needs the threat model in order to behave slightly differently on different codebases. That way, CodeQL can include (or exclude) the appropriate sources of tainted data during its analysis, and flag up the most relevant security vulnerabilities to developers who work on the code.

CodeQL's default threat model works for the vast majority of codebases. It considers data from remote sources (such as HTTP requests) as tainted. Using new CodeQL threat model settings for Java, you can now optionally mark local sources of data as tainted. This includes data from local files, command-line arguments, environment variables, and databases. You can enable the local threat model option in code scanning to help security teams and developers uncover and fix more potential security vulnerabilities in their code.

CodeQL threat model settings can be configured in repositories running code scanning with CodeQL via default setup in the GitHub UI. Alternatively, you can specify it through advanced setup (in an Actions workflow file).

If your repository is running code scanning default setup on Java code, go to the Code security and analysis settings and click Edit configuration under Code scanning default setup. Here, you can change the threat model to Remote and local sources. For more information, see the documentation on including local sources of tainted data in default setup.

If your repository is running code scanning advanced setup on Java code, you can customize the CodeQL threat model by editing the code scanning workflow file. For more information, see the documentation on extending CodeQL coverage with threat models. If you run the CodeQL CLI on the command-line or in third party CI/CD, you can specify a --threat-model when running a code scanning analysis. For more information see the CodeQL CLI documentation.

CodeQL threat model settings (beta) in code scanning default setup is available on GitHub.com for repositories containing Java code. It will be shipped in GitHub Enterprise Server 3.13.

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Code scanning default setup is now available for self-hosted runners on GitHub.com. To use default setup for code scanning, assign the code-scanning label to your runner. Default setup now uses actions/github-script instead of the GH CLI. If your organization has a policy which limits GitHub Actions you will need to allow this action in your policy.

Code scanning sees assigned runners when default setup is enabled. As a result, if a runner is assigned to a repository which is already running default setup, you must disable and re-enable default setup to initiate using the runner.

Larger runners are in beta support, with the limitations that you can only define one single larger runner at the org level with the label code-scanning, and Swift analysis is not supported.

For more information, see “Using labels with self-hosted runners.”

Runner with code-scanning label

This is now available on GitHub.com. Self-Hosted runners for default setup are already supported from GitHub Enterprise Server 3.9.

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In the secret scanning list view, you can now apply a filter to display alerts that are the result of having bypassed push protection. This filter can be applied at the repository, organization, and enterprise levels from the sort menu in the list view UI or by adding bypassed:true to the search bar.

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CodeQL 2.15.4 is rolling out to users of GitHub code scanning on github.com this week, and all new functionality will also be included in GHES 3.12. Users of GHES 3.11 or older can upgrade their CodeQL version.

Important changes in this release include:

  • Performance improvements on large runners (instances with 8 to 16 vCPUs) lead to a reduction in end to end analysis time between 5% and 15%, due to more effective parallelization. Where possible, upgrading to larger instances is recommend for projects that currently use 4 or fewer vCPUs and take more than 10 minutes to analyze.
  • Analysis times for C and C++ code bases of any size are reduced on average by 6%
  • TypeScript 5.3, Java 21 and Python 3.12 are now supported.
  • We have resolved a problem causing scan timeouts on macOS (the default for Swift analysis). This problem affected up to 10% of scans for some projects. Although timeouts may still occur, they are now expected in less than 0.5% of scans. We are actively addressing the remaining issues.

For a full list of changes, please refer to the complete changelog for version 2.15.4.

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Reduce pull request noise and fix multiple security alerts at once with Dependabot grouped security updates.

Starting today, you can enable grouped security updates for Dependabot at the repository or organization-level. When you click “Enable” for this feature, Dependabot will collect all available security updates in a repository and attempt to open one pull request with all of them, per ecosystem, across directories. There is no further configuration available at this time.

Known limitations

  • Dependabot will NOT group across ecosystem (e.g. it will not group pip updates and npm updates together)
  • Dependabot WILL group across directories (e.g. if you have multiple package.json’s in different directories in the same repository)
  • If you have version updates enabled as well, Dependabot will NOT group security updates with version updates
  • If you use grouping for version updates, your groups configuration in dependabot.yml will NOT apply to security updates

To enable this feature, go to your repository or organization settings page, then go to the Code security and analysis tab, and click “Enable” for grouped security updates (this also requires each affected repository to enable Dependency graph, Dependabot alerts, and Dependabot security updates). When you enable this feature, Dependabot will immediately attempt to create grouped security pull requests for any available security updates in your repository.

We’d love to hear your feedback as you try this feature! Join the discussion within GitHub Community.

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GitHub Advanced Security users can now use the REST API to enable or disable secret scanning validity checks for a repository, organization, or enterprise. Validity checks retrieve a status for supported tokens from their relevant partner (active, inactive, or unknown). This status is displayed in the secret scanning alert view and the REST API.

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We have partnered with our sister team at Microsoft to bring some improvements to the NuGet ecosystem for Dependabot updates:

  • Updater logic re-written in C#, making it easier for users of NuGet to contribute to dependabot-core
  • Improvement in detection of where package dependencies are declared in .NET projects
  • Improved support for implicit dependencies
  • Improved support for peer dependencies

Learn more about Dependabot.

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CodeQL 2.15.3 is rolling out to users of GitHub code scanning on github.com this week, and all new functionality will also be included in GHES 3.12. Users of GHES 3.11 or older can upgrade their CodeQL version.

Important changes in this release include:

For a full list of changes, please refer to the complete changelog for version 2.15.3.

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Auto-triage rules are a powerful tool to help you reduce false positives and alert fatigue substantially, while better managing your alerts at scale. We've heard your feedback, which is helping us improve throughout this beta period.

Starting today, you can now create Dependabot auto-triage rules using CVE IDs or GHSA IDs to target subsets of alerts.

How do I learn more?

How do I provide feedback?

Let us know what you think by providing feedback — we’re listening!

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We're simplifying how Dependabot operates! Previously, if Dependabot encountered errors in its last run, it would automatically re-run the job when there were changes in the package manifest (like adding or changing dependencies). This often led to Dependabot running more than needed and creating unscheduled pull requests. To streamline the process and stick to the schedules you set, this automated re-run feature is being deprecated.

Dependabot will still run jobs according to your schedule, and you'll have the option to manually trigger jobs whenever necessary.

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