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. GuardDuty might come to mind when reading exfiltrate them above, however, we dont actually need to worry about GuardDuty here. Permissions to the following For example, the solution enforces usage of VPC isolation with private subnets and usage of the security groups for SageMaker notebook instances, processing, training, and tuning jobs, as well as for models for the SageMaker execution role: For a detailed discussion of the security controls and best practices, refer to Building secure machine learning environments with Amazon SageMaker. The data science environment VPC can be configured with internet access via an optional NAT gateway. See Deploy compute resources with an instance profile. Along with this post, were releasing an updated version of aws_escalate.py with all of our privilege escalation methods aggregated into it. pre-annotation and post-annotation Lambda functions, see Required Permissions To Use Lambda With Ground Truth. Here are the steps I followed: ask init -p officialProfile Logged in with IAM user credential (Email and password) created by my employer Got the success message saying that the profile has been created After doing some research, I created a policy under aws console and added the following JSON to it. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet those needs can sometimes be an inhibitor to agility. Because /opt/python/lib/python3.7/site-packages and /opt/python are second and third in the list, any libraries we include in those folders will be imported before Python even checks the rest of the folders in the list (except /var/task at number 1). dataset if dataset sampling is selected You can find the new version of aws_escalate.py. 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. in the account in AWS KMS. 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. Remove the mapping of your new role to your users: If using Studio with IAM, delete any new Studio users you created. Lambda Layers are a somewhat new feature to AWS Lambda, so for anyone unfamiliar, you can read up on it here. 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. Endorsed by industry leaders, Rhino Security Labs is a trusted security advisor to the Fortune 500. the Lambda function is invoked, but instead every time a new container is launched to handle a Lambda invocation. 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. It continues to be one of the most prevalent issues that our cloud pentesters encounter when attacking AWS environments. Which policy i am missing? Ebook: Questions For Every Pentest Vendor, In part 1 of this series, you can find details on 21 different privilege escalation methods in AWS. With the new SageMaker Role Manager, you can use the combination of personas, pre-built ML activities, and custom policies to quickly generate customized roles in minutes. In this section, you can add or remove additional ML activities to tailor this role to your specific use case. sagemaker:DescribeTrainingJob action. Note that it is required to attach an IAM role to a new Jupyter notebook through SageMaker. The related GuardDuty finding is, UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration. Role Manager offers predefined personas and ML activities combined with a wizard to streamline your permission generation process, allowing your ML practitioners to perform their responsibilities with the minimal necessary permissions. When the instance is initialized or restarts, it enters a pending status and a reverse shell is opened to the attacker machine. The policy is added to the AmazonSageMaker-ExecutionRole that is created when you onboard to Amazon SageMaker Studio. This means that when we run something like import boto3 in our Python function, the function will first look in /var/task. There are several personas currently supported, including: For a comprehensive list of personas and additional details, refer to the persona reference of the SageMaker Role Manager Developer Guide. A user can pass a role ARN as a parameter in any API operation that uses the role to assign permissions to the service. The first thing we wanted to try was to access the Metadata Endpoint, see below. Building secure machine learning environments with Amazon SageMaker, Configuring Amazon SageMaker Studio for teams and groups with complete resource isolation, Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines, SageMaker MLOps template for model deployment, Build a Secure Enterprise Machine Learning Platform on AWS, Setting up secure, well-governed machine learning environments on AWS, IAM roles and cross-account permission setup, Application stack consisting of Studio and SageMaker MLOps projects. 2023, Amazon Web Services, Inc. or its affiliates. Along with this post, were releasing an updated version of aws_escalate.py with all of our privilege escalation methods aggregated into it. In his spare time, he enjoys playing video games, programming, watching sports, and building things. As we can see from the list, /var/runtime is number four in our list, meaning that we can include our own version of boto3 in our Lambda layer and it will be imported instead of the native boto3 library. The user is blocked from running jobs without VPC or AWS KMS configuration, if the role were customized to do so, The user only has access to Amazon S3 resources if the role had the ML activity included, The user is only able to deploy endpoints if the role had the ML activity included. Thanks for letting us know this page needs work. From there, we can do whatever we want with the credentials we steal from the instance. worker UI and verify that input data, labels, and instructions display People Outside of My AWS Account to Access My SageMaker These permissions are Its clear that when youre thinking about cloud risk, your security teams need to think outside of the usual AWS suspects. These permissions are required to allow the Databricks cluster to: Upload permission-scoped objects to S3 for use by SageMaker endpoint servers. 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. 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 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. 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. Because of this, it is especially important to take these methods seriously and follow best practices in your AWS environments. Data access from the Studio notebooks or any SageMaker workload to the environments S3 buckets is governed by the combination of the S3 bucket and user policies and S3 VPC endpoint policy. Please be sure to answer the question.Provide details and share your research! In this second part of the series, we will be discussing 3 new privilege escalation methods that our team has been taking advantage of in our pentests. You can add the following statement to the policy in Grant IAM Permission to Use the On the IAM console, you can view your newly created role along with the attached policies that map the ML activities you selected in Role Manager. Click here to return to Amazon Web Services homepage, Identity and Access Management for AWS CloudTrail. Essentially, Lambda Layers allow you to include code in your Lambda functions and is stored separately from your functions code, in a layer. grant access to the Amazon S3 buckets that contain input and output data. Identify who can create new lifecycle configurations and attach them to a notebook instance. In this section, you create aservice role and then reference it when you create your other personas via PassRole. 1. It will also now scan for IAM users and roles, not just users like before. entity permission to subscribe to a vendor 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. Paste in the instance profile ARN associated with the AWS role you created. We could try to hide our malicious code in a different place in case anyone looks here, but were not going to worry about that now. Paste and save the following JSON definition: where account-id is the ID of the account running the AWS SageMaker service and After you have filled in the required values, choose. This role is used by the SageMaker service to perform operations on the users behalf. With the following code, well install boto3 version 1.9.42 and its dependencies to a local lambda_layer folder: It might not be necessary to install all the dependencies since theyre already in the runtime, but well be doing so just to be safe. users from performing actions that are not used to create and monitoring a 1S3. Discover what an S3 bucket is and how AWS handles access and permissions. Now that you have created the base service roles for your other personas to use, you can create your role for data scientists. 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). In this walkthrough, you perform all the steps to grant permissions to an ML administrator, create a service role for accessing required dependencies for building and training models, and create execution roles for users to assume inside of Studio to perform their tasks. SageMaker project templates also support a CI/CD workflow using Jenkins and GitHub as the source repository. Note that if youre using the IAM Identity Center integration with Studio, the IAM role in this section isnt necessary. custom labeling workflow. The model registry stores the model metadata, and all model artifacts are stored in an S3 bucket (Step 1 in the preceding diagram). As described in AWSs. The following diagram shows the overview of the solution architecture and the deployed components. labeling workflow, Required Permissions To Use Lambda With Ground Truth, Create and Manage Amazon Cognito Workforce, Grant IAM Permission to Use the A persona is an entity that needs to perform a set of ML activities and uses a role to grant them permissions. Because of this, we devote many resources towards research and tool development built around privilege escalation. Note that the following policy locks down Studio domain creation to VPC only. It is used by thousands of leading organizations worldwide. He is passionate about building governance products in Machine Learning for enterprise customers. These include permissions to describe the labeling job status To give the user for an AWS pentest from Rhino, where we take various approaches to discover, demonstrate, and report security misconfigurations and threats in your environment. You can assign fine-grained permissions policies on the least privilege principle to various SageMaker execution roles, used to run different workloads, such as processing or training jobs, pipelines, or inference. 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. When creating roles for your ML practitioners to perform activities in SageMaker, they need to pass permissions to an service role that has access to manage the underlying infrastructure. To allow your data scientists to assume their given persona via the console, they require a console role to get to the Studio environment. Apache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. Resources, How Amazon SageMaker Works with permission to select pre-existing pre-annotation and post-annotation Lambda The related GuardDuty finding is UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration, which will alert if a roles credentials are stolen from an EC2 instance and used elsewhere. Custom Labeling Workflow The deployment of all AWS Service Catalog products happens under a specified service role with the defined set of permissions, which are unrelated to the users permissions. We also look at MLOps automation workflows with SageMaker projects and Pipelines. Start notebook - the script code will be executed when the notebook instance is started or restarted, including its initial creation. For this first pitfall, understanding policy evaluation logic is key. Sometimes people put sensitive info in their Lambda environment variables, which is another benefit to stealing them. In order for your users to access Studio, they need to be associated with the user execution role you created (in this example, based on the data scientist persona). These are, no VPC, a VPC attached with internet access, and a VPC attached without internet access. permission to list and invoke Lambda functions that are used to run a This process must fulfill your organizations operational and security requirements. Luckily for us, this EC2 instance doesnt actually live in our account, but instead it is a managed EC2 instance hosted in an AWS-owned account. IAM, Providing access to an IAM user in another AWS account that you workforce. In this blog we want to dig a little deeper into IAM by explaining 10 pitfalls you should look out for when you configure AWS IAM. If you've got a moment, please tell us what we did right so we can do more of it. With the release of this blog, Rhino now has three separate blog posts on various IAM privilege escalation methods in AWS, . labeling job. This service role can be reused, and doesnt need to be created for every use case. Your administrator is the person It is possible to use access keys for an AWS user with similar permissions as the IAM role specified here, but Databricks recommends 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). This ARN is of the form arn:aws:iam::
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