Constant integration and continuous delivery (CI/CD) misconfigurations learned in the open up-source TensorFlow device understanding framework could have been exploited to orchestrate offer chain attacks.
The misconfigurations could be abused by an attacker to “carry out a supply chain compromise of TensorFlow releases on GitHub and PyPi by compromising TensorFlow’s establish agents by means of a malicious pull ask for,” Praetorian researchers Adnan Khan and John Stawinski mentioned in a report revealed this 7 days.
Effective exploitation of these issues could permit an exterior attacker to add destructive releases to the GitHub repository, get remote code execution on the self-hosted GitHub runner, and even retrieve a GitHub Particular Entry Token (PAT) for the tensorflow-jenkins consumer.
TensorFlow works by using GitHub Steps to automate the program build, exam, and deployment pipeline. Runners, which refer to equipment that execute careers in a GitHub Steps workflow, can be both self-hosted or hosted by GitHub.
“We advise that you only use self-hosted runners with personal repositories,” GitHub notes in its documentation. “This is mainly because forks of your community repository can probably operate risky code on your self-hosted runner equipment by developing a pull ask for that executes the code in a workflow.”
Place in another way, this will allow any contributor to execute arbitrary code on the self-hosted runner by publishing a malicious pull request.
This, however, does not pose any security problem with GitHub-hosted runners, as each runner is ephemeral and is a clear, isolated digital device which is ruined at the stop of the work execution.
Praetorian stated it was in a position to recognize TensorFlow workflows that were executed on self-hosted runners, subsequently acquiring fork pull requests from earlier contributors that routinely induced the suitable CI/CD workflows with no demanding approval.
An adversary seeking to trojanize a focus on repository could, for that reason, take care of a typo or make a modest but genuine code alter, generate a pull request for it, and then hold out until the pull request is merged in buy to come to be a contributor. This would then enable them to execute code on the runner sans increasing any pink flag by generating a rogue pull request.
More examination of the workflow logs discovered that the self-hosted runner was not only non-ephemeral (therefore opening the doorway for persistence), but also that the GITHUB_TOKEN permissions related with the workflow arrived with comprehensive write permissions.
“Since the GITHUB_TOKEN had the Contents:write permission, it could add releases to https://github[.]com/tensorflow/tensorflow/releases/,” the scientists explained. “An attacker that compromised a single of these `GITHUB_TOKEN’s could insert their own information to the Launch Property.”
On major of that, the contents:create permissions could be weaponized to drive code right to the TensorFlow repository by covertly injecting the malicious code into a function branch and acquiring it merged into the principal branch.
Which is not all. A menace actor could steal the AWS_PYPI_ACCOUNT_TOKEN used in the launch workflow to authenticate to the Python Offer Index (PyPI) registry and upload a malicious Python .whl file, properly poisoning the bundle.
“An attacker could also use the GITHUB_TOKEN’s permissions to compromise the JENKINS_TOKEN repository solution, even even though this top secret was not utilised within just workflows that ran on the self-hosted runners,” the researchers explained.
Subsequent dependable disclosure on August 1, 2023, the shortcomings were being resolved by the task maintainers as of December 20, 2023, by necessitating approval for workflows submitted from all fork pull requests and by shifting the GITHUB_TOKEN permissions to go through-only for workflows that ran on self-hosted runners.
“Very similar CI/CD attacks are on the increase as additional organizations automate their CI/CD processes,” the researchers stated.
“AI/ML corporations are especially susceptible as several of their workflows call for considerable compute power that isn’t obtainable in GitHub-hosted runners, so the prevalence of self-hosted runners.”
The disclosure arrives as the two scientists uncovered that several general public GitHub repositories, together with those people linked with Chia Networks, Microsoft DeepSpeed, and PyTorch, are inclined to malicious code injection by means of self-hosted GitHub Actions runners.
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