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We mainly focus on attacks that can be triggered by external attackers, though ideally compromised committer accounts should also be considered.

Helpful tools

There are some validation tools that can help you identify risky patterns in your actions:

• Zizmor can be ran standalone or as a workflow
• CodeQL "Actions" scanning
• The ASF Infra team has a scanner for flagging obvious policy violations
• The ASF Tooling team is working on a scanner, though results are not public yet.

Workflow approval

By default, ASF repositories require approval before building PRs by non-committers.This has some limitations:

• This restriction does not apply to workflows triggered by pull_request_target or issue_comment
• This restriction does not protect against compromised committer accounts
• When using the 'checkout' action to check out the relevant PR, do not specify the ref  as "refs/pull/${{ github.event.number }}/merge" or "github.event.pull_request.head.ref": the PR may have been updated since the workflow was approved. Instead, leave the ref  empty to use the code associated with the event that triggered the build (which should what it looked like when it was approved), or use the exact commit hash.

Action allowlisting

The ASF GitHub org only allows action versions that have been added to the org-wide allowlist. You're invited to add actions you'd like to use to this allowlist, and review new versions as they appear. 

Default GITHUB_TOKEN permissions

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If you think you cannot avoid dangerous workflows - it's best if you reach out to #builds slack channel on ASF slack - there is a group of peple there who discuss various ways you can design your Github Actions in the way to avoid dangerous workflows.

It's highly recommended to use https://woodruffw.github.io/zizmor/ static analysis tool in your CI / pipelines to detect and fix potential security issues in your workflowsThere are some typical tasks done wit pull_request_target - for example labeling PRs with https://github.com/actions/labeler. Those could be often replaced with GitHub Apps - for example there is a "Boring Cyborg" GitHub App: https://github.com/kaxil/boring-cyborg that has similar functionality (among others). Also Boring Cyborg could be extended if some functionalities are missing. PRs are most welcome.

Builds triggered with pull_request_target 

Builds triggered by the pull_request_target event (as opposed to pull_request) by default check out the 'target' branch of the PR, and run with a 'permissive' GITHUB_TOKEN. It is common for such workflows to switch to As long as only code from the trusted/reviewed 'target' branch is executed, this is fine.

Some such workflows switch to the code from the PR that triggered the workflow. This is dangerous, as any code that is loaded from the repo and run after the switch (including actions, scripts, build tools and test code) may be untrusted, and will have access to the GITHUB_TOKEN. Therefore we recommend against doing this, and suggest to split the job into an untrusted pull_request-triggered part and a trusted workflow_run part as documented in the next section:

Builds triggered with workflow_run

A common technique for building untrusted code but also using privileges to act on the build result is to split the build into two parts: a low-privilege one triggered by pull_request that runs the untrusted code, stores the result in an artifact, and triggers a second, high-privilege build with workflow_run  that acts on that result.

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• Always extract into a directory separate from the trusted code in a step before checking out said  trusted code. This stops files extracted from the archive from clobbering trusted code.
• A separate directory can also prevent files in the artifact impersonating often used python modules like pip  as running python -m pip ...  would execute a pip.py file in the cwd
• Validate any content you retrieve from an artifact before you use it to avoid command injection, especially in steps using bash  and Github Actions macros.
  • This includes using cat  on such files as well as putting their content into environment variables (a popular exploit is to modify LD_PRELOAD, some examples ), step outputs to use via ${{ steps.id.outputs.sus_content }} to be used in e.g. if  in bash

An example of a job that is split into two parts like this is the NuttX PR labeler, https://github.com/apache/nuttx/blob/master/.github/workflows/labeler.yml and https://github.com/apache/nuttx/blob/master/.github/workflows/pr_labeler.yml.

Builds triggered with issue_comment 

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