advanced-security

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Secret scanning AI-generated custom patterns (public beta)

Secret scanning now helps you more easily define custom patterns with GitHub Copilot.

As of today, you can leverage AI to generate custom patterns without expert knowledge of regular expressions.

Generate a secret scanning custom pattern with AI

What’s changing?

You can create your own custom detectors for secret scanning by using custom patterns. Formatted as regular expressions, these custom patterns can be challenging to write. Secret scanning now supports a pattern generator backed by GitHub Copilot in order to generate regular expressions that match your input.

How do I use the regular expression generator?

When defining a custom pattern, you can select “generate with AI” in order to launch the regular expression generator.

The model returns up to three regular expressions for you to review. You can click on the regular expression to get an AI-generated plain language description of the regular expression. You should still review this input and carefully validate performance of results by performing a dry run across your organization or repository.

Who can use the regular expression generator?

Anyone able to define custom patterns is able to use the regular expression generator. This feature is shipping to public beta today for all GitHub Enterprise Cloud customers with GitHub Advanced Security.

Learn more about the regular expression generator or how to define your own custom patterns.

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Group Configuration Options for Dependabot Security Updates – Public Beta

Dependabot security updates help you keep your dependencies secure by opening pull requests when a Dependabot alert is raised. With today’s release, you can now use flexible grouping options in dependabot.yml to control how Dependabot structures its security pull requests to make them more mergeable for you based on your context. Whether you’d like to simply update as many dependencies at once as possible (patterns: '*') or minimize the risk of breaking changes (dependency-type: development or update-types: "patch"), there are grouping options for you.

By specifying applies-to: security-updates in your group rule configuration, you can specify how you would like Dependabot to group your security updates. If you would like Dependabot to group together all possible updates for an ecosystem, you can instead use the UI located in your repository settings to do so. To learn more about this, check out our documentation here.

The available grouping options are:

  • patterns, which will match based on package names
  • dependency-type, which will group based on development or production dependencies, for ecosystems where this is supported, and
  • update-types, which will group based on SemVer level update

Learn more about grouping configuration options here.

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CodeQL 2.16.3: AI-powered autofixes for Python, updated queries, and security fixes

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: New Android queries and improved precision

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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Secret scanning adds validity checks for Mailgun and Mailchimp

Secret scanning is extending validity check support to Mailgun (mailgun_api_key) and Mailchimp (mailchimp_api_key) API keys.

Validity checks indicate if the leaked credentials are active and could still be exploited. If you’ve previously enabled validation checks for a given repository, GitHub will now automatically verify validity for alerts on supported token types.

Validity checks are available for repositories with GitHub Advanced Security on Enterprise Cloud. You can enable the feature at both organization and repository levels from the “Code security and analysis” settings page by checking the option to “automatically verify if a secret is valid by sending to the relevant partner.”

Learn more about secret scanning or our supported patterns for validity checks.

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Dependabot support for pub private repositories and registries

If you use private hosted pub repositories or registries to manage your Dart dependencies, Dependabot can now automatically update those dependencies. By adding the details of the private repository or registry to dependabot.yml, Dependabot will be able to access and update these dependencies.

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Secret scanning adds webhook support for validity checks

The secret_scanning_alert webhook is sent for activity related to secret scanning alerts. Secret scanning webhooks now support validity checks, so you can keep track of changes to validity status.

Changes to the secret_scanning_alert webhook:

  • A new validity property that is either active, inactive, or unknown depending on the most recent validity check.
  • A new action type, validated, which is triggered when a secret’s validity status changes.

Note: you must enable validity checks at the repository or organization level in order to opt in to the feature. This can be done from your secret scanning settings on the Code security and analysis settings page by selecting the option to “automatically verify if a secret is valid by sending it to the relevant partner.”

Learn more about which secret types are supported or the secret scanning webhook.

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Secret scanning adds validity checks for Stripe, Telegram, SendGrid, and more

Secret scanning is extending validity check support to several additional token types.

Validity checks indicate if the leaked credentials are active and could still be exploited. If you’ve previously enabled validation checks for a given repository, GitHub will now automatically verify validity for alerts on supported token types. In addition to token types announced in our previous changelogs, you will now see validity checks for the following token types:

Provider Token
Dropbox dropbox_short_lived_access_token
Notion notion_integration_token
OpenAI openai_api_key
OpenAI openai_api_key_v2
SendGrid sendgrid_api_key
Stripe stripe_api_key
Stripe stripe_test_secret_key
Telegram telegram_bot_token

Validity checks are available for repositories with GitHub Advanced Security on Enterprise Cloud. You can enable the feature at both organization and repository levels from the “Code security and analysis” settings page by checking the option to “automatically verify if a secret is valid by sending to the relevant partner.”

Learn more about secret scanning or our supported patterns for validity checks.

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Code scanning can now be enabled on repositories before they contain CodeQL supported languages

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: Swift 5.9.2 Support, New Queries, and Scanned-File Count Changes

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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Dependabot Version Updates Support devcontainers

If you use devcontainer.json files to define your development containers, you will now be able to use Dependabot version updates to keep your Features up-to-date. Once configured in dependabot.yml, Dependabot will open pull requests on a specified schedule to update the listed Features to latest. This ensures Features are pinned to the latest major version in the associated devcontainer.json file. If a dev container has a lockfile, that file will also be updated. Dependabot security updates for dev containers are not supported at this time.

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CodeQL 2.16: Python Dependency Installation Disabled, New Queries, and Bug Fixes

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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Code scanning: deprecation of CodeQL Action v2

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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Canva is now a GitHub secret scanning partner

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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Code scanning is now more adaptable to your codebase with CodeQL threat model settings for Java (beta)

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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