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The CodeQL for Visual Studio Code documentation is now on docs.github.com.

This migrates the content from https://codeql.github.com/docs/codeql-for-visual-studio-code and provides a consistent, single-site experience with improved text, descriptions, images, and navigation.

On May 8, 2024, we’ll begin automatically redirecting from the original codeql.github.com location to the new location.

The source files now exist in Markdown format in the public, open-source docs repository. If you would like to contribute, you can consult and follow the steps listed in the GitHub Docs contributing guide.

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You can now add organisation-level CodeQL model packs to improve code scanning coverage for your GitHub organization. This ensures that custom libraries and frameworks are recognised by CodeQL.

In most cases, the out-of-the-box CodeQL threat models provide the best coverage for identifying potential vulnerabilities in your GitHub repositories using code scanning. The CodeQL team at GitHub keeps a close eye on the most widely-used open-source libraries and frameworks to ensure CodeQL recognizes untrusted data that enters an application. For cases which cannot be covered by default, such as custom-built or inner-sourced frameworks and libraries, you can create custom CodeQL model packs to help CodeQL detect additional security vulnerabilities in your code.

Configuring CodeQL model packs in the organisation code security and analysis settings

When you configure CodeQL model packs at scale, the packs will be used in every code scanning analysis that uses default setup in the organization. By default, code scanning will download the latest version of each model pack, meaning that the latest changes to the pack (such as adding information about new frameworks) will automatically be included. Alternatively, you can configure specific sets of CodeQL models to use by stating a specific version (or version range). For more information, see Editing your configuration of default setup in the GitHub documentation.

You can use the CodeQL model editor in VS Code to easily create custom CodeQL model packs for libraries and frameworks written in C# and Java/Kotlin. Custom CodeQL model packs are also supported for code written in JavaScript and Ruby and we will be adding support for these and other CodeQL-supported languages in the CodeQL model editor in the future.

This functionality is now available on GitHub.com and will be available in GitHub Enterprise Server 3.14.

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CodeQL is the static analysis engine that powers GitHub code scanning. CodeQL version 2.17.0 has been released and has now been rolled out to code scanning users on GitHub.com.

Important changes in this release include:

For a full list of changes, please refer to the complete changelog for version 2.17.0. All new functionality will also be included in GHES 3.13. Users of GHES 3.12 or older can upgrade their CodeQL version.

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Use CodeQL threat model settings for C# (beta) to adapt CodeQL’s code scanning analysis to detect the most relevant security vulnerabilities in your 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. We previously released CodeQL threat model settings for Java to allow you to optionally mark local sources of data (such as data from local files, command-line arguments, environment variables, and databases) as tainted in order to help security teams and developers uncover and fix more potential security vulnerabilities in their code. CodeQL threat model settings are now available for C#, meaning that you can now enable similar local sources of taint in your code scanning analysis of code wriitten in C#.

If your repository is running code scanning default setup on C# or 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.

Threat model setting in CodeQL default configuration

If your repository is running code scanning advanced setup on C# or 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.

As part of this work, we made changes to some of the queries included in the default code scanning suite for C# to better align with local and remote threat model settings. As a result you may see slightly fewer alerts when using the default threat model for remote sources. For more information about which queries are impacted, see the changelog for CodeQL 2.17.0.

CodeQL threat model settings (beta) in code scanning default setup is available on GitHub.com for repositories containing Java and C# code. Support for configuring threat model settings for C# will be shipped in GitHub Enterprise Server 3.14. Users of GHES 3.12 or older can also upgrade the version of CodeQL used in code scanning.

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Today, we’re releasing security tool-specific filters for the security overview dashboard and secret scanning metrics page.

Security tool-centric filters in the filter bar drop-down on the overview dashboard

Have you ever wondered, “How well is my organization handling SQL injections?” or “How quickly are we responding to [partner name] secret leaks?” Maybe you’re curious about the pace of updating your npm dependencies. Well, wonder no more!

With our new security tool filters, you can tailor your search to the exact details you’re curious about, giving you a more focused and relevant report for your needs.

Discover the new filters that are designed to transform your security analysis:

  • Dependabot filters: Zero in on a specific ecosystem, package, and dependency scope.
  • CodeQL/third-party filters: Drill down to the rule that matters most to you.
  • Secret scanning filters: Get granular with filters for secret type, provider, push protection bypassed status and validity.

These features are now available as a public beta on GitHub Enterprise Cloud and will be available in GitHub Enterprise Server 3.14.

Learn more about security overview and send us your feedback

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With the 2.16.5 release of CodeQL, we’re introducing a new mechanism for creating a CodeQL database for Java codebases, without relying on a build. This enables organizations to more easily adopt CodeQL for Java projects at scale. Note: this release announcement contains details for users of the CodeQL CLI and advanced setup for code scanning. If you’re using GitHub code scanning default setup (which is powered by the CodeQL engine), this related release announcement will likely contain the information you’re looking for.

Previously, CodeQL required a working build to analyze Java projects. This could either be automatically detected or manually specified. Starting with CodeQL 2.16.5, you can now scan Java code without the need for a build. Our large-scale testing has shown that CodeQL can be successfully enabled for over 90% of Java repos without manual intervention.

This feature is currently in public beta and is accessible to all GitHub.com advanced setup for code scanning and CodeQL CLI users scanning Java code:

  • Repositories using advanced setup for code scanning via workflow files will have the option to choose a build-mode. The default value for newly configured Java repos will be build-mode: none.
  • CodeQL CLI users will not experience any change in the default behaviour, for compatibility with existing workflows. Users that want to enable this feature can now use the --build-mode none option. Generally, we also recommend users set the --build-mode option when using the CLI to make it easier to debug and persist the configuration should default behaviour change at any point in the future.
    codeql database create test_no_build_db --language java --build-mode none

  • Repositories containing a mix of Kotlin and Java code still require a working build for CodeQL analysis.

The new mechanism for scanning Java is available on GitHub.com and in CodeQL CLI 2.16.5. While in public beta, this feature will not be available on GitHub Enterprise Server for default setup or advanced setup for code scanning. As we continue to work on scanning Java projects without the need for working builds, send us your feedback.

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Today, we’re releasing a host of new insights to the security overview dashboard, as well as an enhanced secret scanning metrics page.

New dashboard insights

overview dashboard with third-party tools, the trend indicator for age of alerts, and reopened alerts tile highlighted

  • Third-party alerts integration: Beyond GitHub’s own CodeQL, secret scanning, and Dependabot security tools, you can now view alert metrics for third-party tools directly on the overview dashboard. Use tool:[third-party-tool name] to view metrics for a specific third-party security tool, or tool:third-party to view metrics for all third-party security alerts.
  • Reopened alerts tracking: Uncover recurring vulnerabilities with the new reopened alerts metric tile, which identifies vulnerabilities that have resurfaced after being previously resolved. This data point helps assess the long-term effectiveness of your remediation efforts.
  • Trend indicators: Review changes over time with trend indicators for key metrics like age of alerts, mean time to remediate, net resolve rate, and total alert count. These indicators offer a clear view of performance shifts and trends between a given date range and that same range reflected backward in time.
  • Advisories tab: Stay informed with the new advisories table, which details the top 10 alert advisories affecting your organization, including the advisories’ CVE IDs, ecosystems, open alert counts, and severities.

Secret scanning metrics page enhancements

secret scanning metrics page with filter bar highlighted

You can now refine your insights with filters for dates, repository custom properties, teams, and more on the secret scanning metrics page. These new filters empower you to pinpoint specific repositories and view changes over time, enabling a more targeted analysis. Additionally, if you are an organization member, you can now view metrics for the repositories you have access to.

These features are now available as a public beta on GitHub Enterprise Cloud and will be available in GitHub Enterprise Server 3.13.

Learn more about security overview and send us your feedback

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CodeQL, the static analysis engine that powers GitHub code scanning, can now analyze Java projects without needing a build. This enables organizations to more easily roll out CodeQL at scale. This new way of analyzing Java codebases is now enabled by default for GitHub.com users setting up new repositories with default setup for code scanning.

Previously, CodeQL required a working build to analyze Java projects. This could either be automatically detected or manually specified. By removing that requirement, our large-scale testing has shown that CodeQL can be successfully enabled for over 90% of Java repos without manual intervention.

This feature is currently in public beta and is accessible to all users scanning Java code using default setup for code scanning on GitHub.com:

  • Anyone setting up their repo using code scanning default setup will automatically benefit from this new analysis approach.
  • Repositories containing a mix of Kotlin and Java code still require a working build for CodeQL analysis. CodeQL will default to the autobuild build mode to automatically try and detect the right build command.
  • Repositories with an existing code scanning setup will not experience any changes. If code scanning is working for you today it will continue to work as-is, and there is no need to change your configuration.

GitHub.com users using advanced setup for code scanning and users of the CodeQL CLI will be able to analyze Java projects without needing a working build as part of CodeQL CLI version 2.16.5. While in public beta, this feature will not be available for GitHub Enterprise Server. As we continue to work on scanning Java projects without needing a working build, send us your feedback.

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Starting today, you can take advantage of the new “age” grouping for the alert trends graph and explore enhanced filter options on the security overview dashboard, aimed at improving your analytical process and security management.

alert trends grouped by age

Explore the dynamics of your security alerts with the new alert age grouping on the alert trends graph. This new functionality offers a refined view into the lifecycle of your security alerts, enabling you to better evaluate the timeliness and effectiveness of your response strategies.

New filter options

repository custom property filter on the security overview page

Leverage enhanced filters to fine-tune your security insights on the overview dashboard:
* Custom repository property filters: With repository custom properties, you can now tag your repositories with descriptive metadata, aiding in efficient organization and analysis across security overview.
* Severity filters: Severity-based filters allow you to concentrate on the vulnerabilities that matter most, streamlining the process of security risk assessment and prioritization.
* Improved date picker controls: Navigate through time with ease using the new date picker options, allowing for quick selection of rolling periods like “Last 14 days,” “Last 30 days,” or “Last 90 days.” Bookmark your preferred time window to keep your analysis current with each visit.

You can access these new functionalities in security overview by navigating to the “Security” tab at the organization level.

These features are now available as a public beta on GitHub Enterprise Cloud and will be available in GitHub Enterprise Server 3.13.

Learn more about security overview and send us your feedback

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Code scanning autofix is now available in public beta for all GitHub Advanced Security customers. Powered by GitHub Copilot, code scanning suggests fixes for Javascript, Typescript, Java, and Python alerts found by CodeQL.
This feature empowers developers to reduce the time and effort spent remediating alerts found in pull requests, and helps prevent new vulnerabilities from being introduced into your code base.

Autofix

The feature is automatically enabled on all private repositories for GitHub Advanced Security customers.
When code scanning analysis is performed on pull requests, autofixes will be generated for supported alerts. They include a natural language explanation of the suggested fix, together with a preview of the code suggestion that the developer can accept, edit, or dismiss. In addition to changes to the current file, these code suggestions can include changes to multiple files. Where needed, autofix may also add or modify dependencies.

You can see the total number of autofix suggestions provided for CodeQL alerts in open and closed pull requests in security overview:

Autofixes on the overview dashboard

You can configure code scanning autofix for a repository or organisation. You can also use Policies for Code security and analysis to allow autofix for CodeQL code scanning for an enterprise.

Enterprise settings

Code scanning autofix supports, on average, 90% of CodeQL Javascript, Typescript, Java, and Python alerts from queries in the Default code scanning suite. The fix generation for any given alert also depends on the context and location of the alert. In some cases, code scanning won’t display a fix suggestion for an alert if the suggested code change fails syntax tests or safety filtering.

This change is now available to all GitHub Advanced Security customers on GitHub.com. For more information, see About autofix for CodeQL code scanning.

Provide feedback for code scanning autofix here.

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You can now monitor enablement trends for all security products within your GitHub organization. This functionality is designed to give you a detailed overview of how your organization is implementing security product coverage.

new tool adoption report

Explore enablement trends for historical insights into the activation status of GitHub security features:
* Dependabot alerts
* Dependabot security updates
* Code scanning
* Secret scanning alerts
* Secret scanning push protection

Historical data is available from January 1, 2024, with the exception of Dependabot security updates data, which is available from January 17, 2024.

To access the enablement trends page, visit security overview at the organization level. You can find security overview by clicking on the “Security” tab.

This feature is now available as a public beta on GitHub Enterprise Cloud and will be available in GitHub Enterprise Server 3.13.

Learn more about security overview and join the discussion within the GitHub Community

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CodeQL is the static analysis engine that powers GitHub code scanning. CodeQL version 2.16.4 has been released and has now been rolled out to code scanning users on GitHub.com.

CodeQL code scanning now supports automatic fix suggestions for Java alerts on pull requests, powered by Copilot. This is automatically enabled for all current autofix preview participants. You can sign up for the preview here and use our public discussion for questions and feedback.

The number of generated autofixes is now also visible in a dedicated security overview tile:

security overview showing a counter of fix suggestions

Furthermore, this release

For a full list of changes, please refer to the complete changelog for version 2.16.4. All new functionality will also be included in GHES 3.13. Users of GHES 3.12 or older can upgrade their CodeQL version.

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CodeQL is the static analysis engine that powers GitHub code scanning. CodeQL version 2.16.3 has been released and has now been rolled out to code scanning users on GitHub.com.

Important changes in this release include:

  • CodeQL code scanning now supports AI-powered automatic fix suggestions for Python alerts on pull requests. This is automatically enabled for all current autofix preview participants.
  • A new option has been added to the Python extractor: python_executable_name. This allows you to select a non-default Python executable installed on the system running the scan (e.g. py.exe on Windows machines).
  • A fix for CVE-2024-25129, a low-severity data exfiltration vulnerability that could be triggered by processing untrusted databases or CodeQL packs.
  • Two new queries:
  • The sinks of queries java/path-injection and java/path-injection-local have been reworked to reduce the number of false positives.

For a full list of changes, please refer to the complete changelog for version 2.16.3. All new functionality will also be included in GHES 3.13. Users of GHES 3.12 or older can upgrade their CodeQL version.

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CodeQL 2.16.2 is now available to users of GitHub code scanning on github.com, and all new functionality will also be included in GHES 3.13. Users of GHES 3.12 or older can upgrade their CodeQL version.

Important changes in this release include:

We added two new Java / Android queries (java/android/sensitive-text and java/android/sensitive-notification) to detect sensitive data exposure via text fields and notifications.

We have improved the precision of several C/C++ queries.

We now recognize collection expressions introduced in C# 12 (e.g. [1, y, 4, .. x]).

For a full list of changes, please refer to the complete changelog for version 2.16.2

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Code scanning can now be enabled on repositories even if they don’t contain any code written in the languages currently supported by CodeQL. Default setup will automatically trigger the first scan when a supported language is detected on the default branch. This means users can now enable code scanning using default setup, for example on empty repositories, and have confidence that they will be automatically protected in the future when the languages in the repository change to include supported languages.

This also takes effect from the organization level so you can bulk-enable code scanning on repositories without CodeQL supported languages.

Enabled on repo without supported languages

This change is now on GitHub.com and will be available in GitHub Enterprise Server 3.13. For more information, see “About code scanning default setup.”

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CodeQL 2.16.1 is now available to users of GitHub code scanning on github.com, and all new functionality will also be included in GHES 3.13. Users of GHES 3.12 or older can upgrade their CodeQL version.

Important changes in this release include:

Swift 5.9.2 is now supported.

We added a new query for Swift, swift/weak-password-hashing, to detect the use of inappropriate hashing algorithms for password hashing and a new query for Java, java/exec-tainted-environment, to detect the injection of environment variables names or values from remote input.

We improved the tracking of flows from handler methods of a PageModel class to the corresponding Razor Page (.cshtml) file, which may result in additional alerts from some queries.

JavaScript now supports doT templates and Go added support for AWS Lambda functions and fasthttp framework.

In the previous version, 2.16.0, we announced that we will update the way we measure the number of scanned files in the Code Scanning UI. This change is now live for JavaScript/TypeScript, Python, Ruby, Swift, and C#.

For a full list of changes, please refer to the complete changelog for version 2.16.1.

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CodeQL 2.16.0 is now available to users of GitHub code scanning on github.com, and all new functionality will also be included in GHES 3.13. Users of GHES 3.12 or older can upgrade their CodeQL version.

Important changes in this release include:

In July 2023, we disabled automatic dependency installation for new CodeQL code scanning setups when analyzing Python code. With the release of CodeQL 2.16.0, we have disabled dependency installation for all existing configurations as well. This change should lead to a decrease in analysis time for projects that were installing dependencies during analysis, without any significant impact on results. A fallback environment variable flag is available to ease the transition, but will be removed in CodeQL 2.17.0. No action is required for Default setup users. Advanced setup users that had previously set the setup-python-dependencies option in their CodeQL code scanning workflows are encouraged to remove it, as it no longer has any effect.

We fixed a bug that could cause CodeQL to consume more memory than configured when using the --ram flag. If you have used this flag to manually override the memory allocation limit for CodeQL, you may be able to increase it slightly to more closely match the system’s available memory. No action is required for users of the CodeQL Action (on github.com or in GHES) who are not using this flag, as memory limits are calculated automatically.

We added 2 new C/C++ queries that detect pointer lifetime issues, and identify instances where the return value of scanf is not checked correctly. We added a new Java query that detects uses of weakly random values, which an attacker may be able to predict. Furthermore, we improved the precision and fixed potential false-positives for several other queries.

The measure of scanning Go files in the code scanning UI now includes partially extracted files, as this more accurately reflects the source of extracted information even when parts of a file could not be analyzed. We will gradually roll this change out for all supported languages in the near future.

We fixed a bug that led to errors in build commands for Swift analyses on macOS that included the codesign tool.

For a full list of changes, please refer to the complete changelog for version 2.16.0 and 2.15.5.

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On December 13, 2023, we released CodeQL Action v3, which runs on the Node.js 20 runtime. CodeQL Action v2 will be deprecated at the same time as GHES 3.11, which is currently scheduled for December 2024.

How does this affect me?

Default setup

Users of code scanning default setup do not need to take any action in order to automatically move to CodeQL Action v3.

Advanced setup

Users of code scanning advanced setup need to change their workflow files in order to start using CodeQL Action v3.

Users of GitHub.com and GitHub Enterprise Server 3.12 (and newer)

All users of GitHub code scanning (which by default uses the CodeQL analysis engine) on GitHub Actions on the following platforms should update their workflow files:

  • GitHub.com (including open source repositories, users of GitHub Teams and GitHub Enterprise Cloud)
  • GitHub Enterprise Server (GHES) 3.12 (and newer)

Users of the above-mentioned platforms should update their CodeQL workflow file(s) to refer to the new v3 version of the CodeQL Action. Note that the upcoming release of GitHub Enterprise Server 3.12 will ship with v3 of the CodeQL Action included.

Users of GitHub Enterprise Server 3.11

While GHES 3.11 does support Node 20 Actions, it does not ship with CodeQL Action v3. Users who want to migrate to v3 on GHES 3.11 should request that their system administrator enables GitHub Connect to download v3 onto GHES before updating their workflow files.

Users of GitHub Enterprise Server 3.10 (and older)

GHES 3.10 (and earlier) does not support running Actions using the Node 20 runtime and is therefore unable to run CodeQL Action v3. Please upgrade to a newer version of GitHub Enterprise Server prior to changing your CodeQL Action workflow files.

Exactly what do I need to change?

To upgrade to CodeQL Action v3, open your CodeQL workflow file(s) in the .github directory of your repository and look for references to:

  • github/codeql-action/init@v2
  • github/codeql-action/autobuild@v2
  • github/codeql-action/analyze@v2
  • github/codeql-action/upload-sarif@v2

These entries need to be replaced with their v3 equivalents:

  • github/codeql-action/init@v3
  • github/codeql-action/autobuild@v3
  • github/codeql-action/analyze@v3
  • github/codeql-action/upload-sarif@v3

Can I use Dependabot to help me with this upgrade?

Yes, you can! For more details on how to configure Dependabot to automatically upgrade your Actions dependencies, please see this page.

What happens in December 2024?

In December 2024, CodeQL Action v2 will be officially deprecated (at the same time as the GHES 3.11 deprecation). At that point, no new updates will be made to CodeQL Action v2, which means that new CodeQL analysis capabilities will only be available to users of CodeQL Action v3. We will keep a close eye on the migration progress across GitHub. If many workflow files still refer to CodeQL Action v2, we might consider scheduling one or more brownout moments later in the year to increase awareness.

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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.

Threat model setting in CodeQL default configuration

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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