Lets look at each group of components in more detail. The next step would be to either invoke the function ourselves if we can or to wait until it gets invoked by normal meanswhich is the safer method. After youre logged in to Studio, you can use the following example notebook to validate the permissions that you granted to your data science user. Implicit denies. The following diagram shows the deployment architecture for the solution. If the SageMaker console in your web browser is used to create a supported role, it will have the AmazonSageMakerFullAccess IAM policy attached to it, which grants a lot of read+write access to multiple different AWS services. The solution uses IAM to set up personas and service execution roles (5). That policy allows the use of lambda:CreateFunction and lambda:UpdateFunctionConfiguration, but it requires that any layers that are used are from within your own account and not a third-party account, like shown in the example above. He has over 20 years of experience working at all levels of software development and solutions architecture and has used programming languages from COBOL and Assembler to .NET, Java, and Python. policy above. | Privacy Policy | Terms of Use, arn:aws:iam:::role/, "arn:aws:s3:::mlflow-sagemaker-*-", "arn:aws:s3:::mlflow-sagemaker-*-/*", "arn:aws:iam:::role/", "arn:aws:ecr:*::repository/*", arn:aws:iam:::instance-profile/, Configure S3 access with instance profiles, Deploy compute resources with an instance profile, Get started with Databricks administration, Create and manage your Databricks workspaces, Manage users, service principals, and groups, Set up AWS authentication for SageMaker deployment, Configure Databricks S3 commit service-related settings, Enforce AWS Instance Metadata Service v2 on a workspace. The solution presented in this post is built for an actual use case for an AWS customer in the financial services industry. grant access to the Amazon S3 buckets that contain input and output data. execution role used to create the labeling job to SageMaker to render and preview the Also, make sure that the layer is compatible with Python 3.7 and that the layer is in the same region as our target function. The templates include the seed code repository with Studio notebooks, which implements a secure setup of SageMaker workloads such as processing, training jobs, and pipelines. An attacker with the iam:PassRole and sagemaker:CreateNotebookInstance permissions can create a new notebook instance. It also allows you to share code across many functions, so you dont have to manage the same code in multiple places. As a result of our research, here are three actionable tips for data science teams to ensure secure use of AWS SageMaker. for an AWS pentest from Rhino, where we take various approaches to discover, demonstrate, and report security misconfigurations and threats in your environment. On the left in the terminal, we can see aws_escalate.py being ran with the DemoKeys AWS CLI profile (the -p argument). SageMaker Role Manager provides you with a set of predefined persona templates for common use cases, or you can build your own custom persona. labeling workflow. To narrow the scope of this policy, add the role ARN of the execution Choose a base persona template to give your persona a baseline set of ML activities. These actions are If your ML practitioners access SageMaker via the AWS Management Console, you can create the permissions to allow access or grant access through IAM Identity Center (Successor to AWS Single Sign-On). The method of associating the user with the role varies based on the authentication method you set up for your Studio domain, either IAM or IAM Identity Center. Note that the following policy locks down Studio domain creation to VPC only. If you havent read this first post, you can check it out, Lambda Layers are a somewhat new feature to AWS Lambda, so for anyone unfamiliar, you can read up on it. By default, the CodeBuild project will not run within a VPC, the image will be pushed to a repository sagemakerstudio with the tag latest, and use the Studio App's execution role and the default SageMaker Python SDK S3 bucket. Under the Other section, click the Terminal button, which can be seen here. Add a policy to your SageMaker role in IAM If you are running this notebook on an Amazon SageMaker notebook instance, the IAM role assumed by your notebook instance needs permission to create and run workflows in AWS Step Functions. We must create the following IAM roles in the staging and production accounts: These roles are assumed by the roles in the development account. This post aims to look at this service in more detail, and to point out the potential risks and misconfigurations that can expose an AWS account to security breaches through the SageMaker service. Some of them are already fixed in production and cannot be reproduced. If you need help, contact your AWS administrator. 1 Answer Sorted by: 8 You need to add iam:PassRole action to the policy of the IAM user that is being used to create-job. It focuses on the security, automation, and governance aspects of multi-account ML environments. This role is used by the SageMaker service to perform operations on the users behalf. in the account in AWS KMS. You can add the following statement to the policy in Grant IAM Permission to Use the This allows your ML practitioners to start working in SageMaker faster. The solution provisions two buckets: Data and Models. Traffic between your VPC and the AWS services doesnt leave the Amazon network and isnt exposed to the public internet. To learn more You can change the existing policies here by selecting the policy and editing the document. The timeout of our exfiltration request is set to 0.1 so that we dont accidentally hang the Lambda function if something is wrong with our C2 server. These are One of the main advantages of using AWS Service Catalog for self-provisioning is that authorized users can configure and deploy available products and AWS resources on their own, without needing full privileges or access to AWS services. one or more functions of Ground Truth. In order to aggregate all of these methods, we have created a repository in our GitHub that will house all of our published privilege escalation methods and how to exploit them. To restrict the keys that a user can list and select, Pipelines is responsible for orchestrating workflows across each step of the ML process and task automation, including data loading, data transformation, training, tuning and validation, and deployment. SageMaker model-hosting endpoints are placed in the VPC (6) in each of the target accounts. This means that an area of the cloud will be cut off from the broader cloud environment accessible only through very specific and secure private networks. To learn more about how to use SageMaker Role Manager, refer to the SageMaker Role Manager Developer Guide. The post Multi-account model deployment with Amazon SageMaker Pipelines shows a conceptual setup of a multi-account MLOps environment based on Pipelines and SageMaker projects. If you have already created an execution role, and want to narrow the scope of After model training and validation, the model is registered in the model registry. Amazon SageMaker Ground Truth Console. Permissions, Private Workforce Paste in the instance profile ARN associated with the AWS role you created. After submitting your persona, you can go to the IAM console and see the resulting role and policies that were created for you, as well as make further modifications. These permissions are required to allow the Databricks cluster to: Upload permission-scoped objects to S3 for use by SageMaker endpoint servers. access the TrainingJob resource using the In this post, we look at how to use Amazon SageMaker Role Manager to quickly build out a set of persona-based roles that can be further customized to your specific requirements in minutes, right on the Amazon SageMaker console. Click here to return to Amazon Web Services homepage, Identity and Access Management for AWS CloudTrail. ago The AmazonSageMakerFullAccess managed policy doesn't contain the permission for sagemaker:CreateDomain (the policy is for accessing SageMaker, not administering it). Permissions, Vendor Workforce There are two ways to create a new Jupyter notebook in Amazon SageMaker: As part of our in-depth research into this Machine Learning service, we compared SageMaker Studio and SageMaker notebook instances, examining their environment and the underlying architecture in terms of security. Although this is a best practice for controlling network access, you need to remove the LockDownStudioDomainCreateToVPC statement if your implementation doesnt use a VPC-based Studio domain. AWS SageMaker role should be attached to the minimum required permissions to avoid risky privilege escalation that might take over the account. The other option would be to contact the EC2 metadata service for the IAM roles credentials directly and exfiltrate them. The following sections list policies you may want to grant to a role to use Build a machine learning workflow using Step Functions and SageMaker You can use the delivered seed code to implement your own customized model deployment pipelines with additional tests or approval steps. statement are used to view Amazon S3 buckets for automated data setup (ListAllMyBuckets), The Amazon ECS agent populates the AWS_CONTAINER_CREDENTIALS_RELATIVE_URI environment variable for all containers that belong to this task, with the following relative URI:/credential_provider_version/credentials?id=task_credential_id, To our surprise, this contradicted the Amazon documentation that said Due to security concerns, access to the Amazon Elastic Compute Cloud (Amazon EC2) Instance Metadata Service (IMDS) is unavailable in SageMaker Studio.. Your roles trust relationships should resemble the following: Go to your Databricks workspace AWS role. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. Abstract. Avoid Security Risks When Working with SageMaker Notebook - Lightspin Because boto3 is being imported outside of the lambda_handler method, we wont receive credentials. required to list, select, and if required, create an execution role in the Rhino Security Labs is a top penetration testing and security assessment firm, with a focus on cloud pentesting (AWS, GCP, Azure), network pentesting, web application pentesting, and phishing. In his spare time, he enjoys riding motorcycle, playing tennis, and photography. All SageMaker workloads, like Studio notebooks, processing or training jobs, and inference endpoints, are placed in the private subnets within the dedicated security group (2). This makes it so your functions deployment package can stay small and so you dont have to repeatedly update the same dependencies to all of your functions individually. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. With manual, deep-dive engagements, we identify security vulnerabilities which put clients at risk. Here is an example screenshot with a few different vulnerable users found in my test environment and the output file opened in Microsoft Excel (it outputs .csv files): On the left in the terminal, we can see aws_escalate.py being ran with the DemoKeys AWS CLI profile (the -p argument). AWS Documentation Amazon SageMaker Troubleshooting Amazon SageMaker Identity and Access PDF RSS Use the following information to help you diagnose and fix common issues that you might encounter when working with SageMaker and IAM. To learn whether SageMaker supports these features, see How Amazon SageMaker Works with Asking for help, clarification, or responding to other answers. Compared to the previous post, this solution implements network traffic and access controls with VPC endpoints, security groups, and fine-grained permissions with designated IAM roles. These can be overridden with the relevant CLI options. You can find the new version of aws_escalate.py here (the old version is still in the old repo). User is not authorized to perform: iam:PassRole on resource The policy above grants a user permission to list and select any key Assigning IAM Identity Center users to execution roles requires them to first exist in the IAM Identity Center directory. 2S3Lambda. this statement so that users can only select that role in the console, specify We also would need to know what libraries they are using, so we can override them correctly, but in this example, well just assume they are importing boto3. That policy allows the use of lambda:CreateFunction and lambda:UpdateFunctionConfiguration, but it requires that any layers that are used are from within your own account and not a third-party account, like shown in the example above. We're sorry we let you down. To see the full list along with template policy details, refer to the ML Activity reference of the SageMaker Role Manager Developer Guide. Such ML platforms and workflows can fulfill stringent security requirements, even for regulated industries such as financial services. A Studio domain consists of a list of authorized users, configuration settings, and an Amazon Elastic File System (Amazon EFS) volume. 1. We realized that we had found a bypass to access the EC2 instance metadata on SageMaker Studio through ECS task metadata. SageMaker project templates also support a CI/CD workflow using Jenkins and GitHub as the source repository. This graphic can provide a visual representation of the environment. To get to the new role in IAM, choose Go to role in the success banner. The bucket policy (2) for the bucket where the models are stored grants access to the ModelExecutionRole principals (5) in each of the target accounts: We apply the same setup for the data encryption key (3), whose policy (4) grants access to the principals in the target accounts. This article describes how to set up instance profiles to allow you to deploy MLflow models to AWS SageMaker. Remove the mapping of your new role to your users: If using Studio with IAM, delete any new Studio users you created. Under Select type of trusted entity, select AWS service. 1 Answer Sorted by: 0 It's clear from the IAM policy that you've posted that you're only allowed to do an iam:PassRole on arn:aws:iam::############:role/query_training_status-role while Glue is trying to use the arn:aws:iam::############:role/AWS-Glue-S3-Bucket-Access. IAM, Providing access to an IAM user in another AWS account that you 3Lambda . You control access to the resources behind a VPC endpoint with a VPC endpoint policy. A user can pass a role ARN as a parameter in any API operation that uses the role to assign permissions to the service. Finally, the model is deployed to a SageMaker endpoint (6). Personas are composed of one or more ML activities to grant permissions. people access to your resources. The project templates are provisioned in Studio via AWS Service Catalog. Because of this, it is especially important to take these methods seriously and follow best practices in your AWS environments. are already subscribed to when creating a labeling job. To learn how to give an entity permission to create and test In this post, you walk through how to use SageMaker Role Manager to create a data scientist role for accessing Amazon SageMaker Studio, while maintaining a set of minimal permissions to perform their necessary activities. GetObject), check for and create a CORS policy in Amazon S3 The MLOps projects implement CI/CD using Pipelines and AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild. First, we must understand how the multi-account CI/CD model pipeline deploys the model to SageMaker endpoints in the target accounts. After you have filled in the required values, choose. See Deploy compute resources with an instance profile. policy. With the release of this blog, Rhino now has three separate blog posts on various IAM privilege escalation methods in AWSpart 1 of this post and the recent CodeStar blog. You can find the new version of aws_escalate.py. From the terminal we have a few options, one of which would be to just use the AWS CLI. This is better for us because that means that not every Lambda invocation will be making outbound requests and our server wont get annihilated with HTTP requests while the original credentials we stole are still valid. An MLOps template, made available through SageMaker projects and provided via an AWS Service Catalog portfolio. This role can also be recreated via Infrastructure as Code (IaC) by simply taking the contents of the policy documents and inserting them into your existing solution. correctly. This statement includes sagemaker:*, which allows the user to IAM User Guide. AWS services don't play well when having a mix of accounts and service as principals in the trust relationship, for example, if you try to do that with CodeBuild it will complain saying it doesn't own the the principal. add the statement in Vendor Workforce The policy is added to the AmazonSageMaker-ExecutionRole that is created when you onboard to Amazon SageMaker Studio. That means that we can exfiltrate these credentials and not worry about triggering our targets GuardDuty detectors. The following screenshot shows the output of printing sys.path in a Python 3.7 Lambda Function. If you must control ingress and egress network traffic or apply any filtering rules, you can use Network Firewall as described in Securing Amazon SageMaker Studio internet traffic using AWS Network Firewall. If using Studio with IAM Identity Center, detach the created execution role from your Studio users. Grant IAM Permission to Use the Amazon SageMaker Ground Truth Console This finding fits Amazons documentation regarding IMDS access where they state, Due to security concerns, access to the Amazon Elastic Compute Cloud (Amazon EC2) Instance Metadata Service (IMDS) is unavailable in SageMaker Studio.. Ozan Ekenis a Senior Product Manager at Amazon Web Services. roles, or to create a new one. Thanks for letting us know this page needs work. This repository also includes a build specification file, used by CodePipeline and CodeBuild to run the pipeline automatically. This is better for us because that means that not every Lambda invocation will be making outbound requests and our server wont get annihilated with HTTP requests while the original credentials we stole are still valid. AWS IAM Privilege Escalation Methods 1. Ram Vittal is a Principal ML Solutions Architect at AWS. The CI/CD pipeline uses CloudFormation stack sets (2) to deploy the model in the target accounts. perform actions required to use certain features of the Ground Truth console. Securing cloud native environments requires companies to move away from their traditional on-premises security concepts. To use the Amazon Web Services Documentation, Javascript must be enabled.

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