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    "text": " I'm Nimrod. I'm a solution architect at BridgeCrew. And over the next 35 minutes or so, I'd love to walk you through how you can utilize open source to do least privilege using infrastructure as code. Sorry. So, a bit about me. I'm a solution architect at BridgeCrew. BridgeCrew is a cloud security company that helps identify misconfigurations in both real cloud environments and infrastructure as code frameworks, such as Terraform, CloudFormation, etc. And we're doing all of this using grid automation. As part of my role in BridgeCrew, I'm involved with a few open source projects around AWS and Terraform, and security, which I either co-maintain or contribute to continuously. And my newest open source tool is called AirIAM, which I hope at the end of this talk you'll also be a fan of. Let's dive in. So, just a quick overview. AWS AirIAM is a service by AWS that manages all the privileges in your AWS account, in every AWS account. That means it includes all the privileges both for things that you need, your employees, your servers, but also for things that run in the background, like AWS services and access across services. So, it starts out really cool, and it allows that for everything. But the problem is that it is hard because it includes a lot of specifics and details. You create a user for every employee, and maybe for integrations, and for your CICD pipeline, and your servers, and it just becomes way too hectic. So, let's try to break it down and see why it is that way. When I look at AIAM, I think of it more like a road system. So, we have our users and our servers, and we have the resources, our data, our buckets, our databases, and all of those are destinations. And IAM, or privileges that are described in IAM, are like the roads between those entities. So, usually when you start out, you don't need a road planner. You have maybe two or three employees, and you set up maybe a server. That server serves your app. It can be behind a load balancer. It's pretty simple stuff. You won't need more than maybe two or three roles and top four or five users. But we don't use the cloud for this simple infrastructure. We use the cloud because it allows us to grow and scale and use more and more services. And sooner rather than later, you'll find yourself stuck in this kind of situation. You'll be adding more services, RDS, ECS, CloudFormation, CloudFront, more and more services, and all of those require different roles and different users. Some of them are maybe even third-party tools. And you also have different access patterns. You can start out by everybody using their own IAM users and then switch out to maybe using SSO and identity providers, which mean you have roles and not set of credentials. And all of that leaves layer over layer of unused and unnecessary privileges, which can be used by bad actors. Now, as a security person, which is kind of what I do, when I see IAM, I have only one question I really want to answer. And that is, who has access to what? It should be pretty simple, but I will now show you why it isn't that simple, specifically in AWS IAM, but that happens also in GCP, Azure, and other cloud providers. So let's start out with a simple question. Who has admin access, which is full access, to my AWS account? When I asked this question in the beginning, naturally I went to the administrator access, which is a managed AWS policy, and I tried to understand who has access to that policy. Who has that policy attached? So you can see here, this is an example account. We have two roles, which have admin access to the account, and also a group. Now, the group in itself is not any user or role, but we would like to know who is part of that group. So I clicked on the group, and now I see the group has 11 users, all of them have admin access to the account. The problem with IAM in AWS is that this isn't a complete list of all the admins in the account. And the reason is simple. IAM allows you to create your own policy documents, which are simple JSONs, in which you describe what you allow and deny for each entity. It could be a role, it could be a user, it could be a group. So this is an example of an inline policy, which means it's a policy for one specific role, which allows all actions on all resources. Well, that's what I would call an admin. And this isn't listed anywhere. It won't show up anywhere. You'll have to go entity by entity, policy document by policy document to find these entities. So this example shows us two problems we have with IAM. One is that it is easily bypassed. You can just create your own policy, it won't be listed anywhere, no one will know, and you can give any permission you'd like. The second one is that IAM supports complex relationships. Let's say you have a user. You can attach a managed policy to it, or you can put an inline policy. But as we've just seen, you can also do that using a group, right? You can have the user be a part of a group, and that group will have the policies attached or put on. But that's not all that IAM supports. IAM supports assuming role as well. So that means a user can assume a role and get the policies that are set on that role, or assume through that role another role. And that's a long chain of privilege excavation that you won't be able to see using simple console and going through the entities in IAM. But that's not all. Let's take a look at another example and see two other things we can find in IAM. So this is a list of the 11 admins from the admin group we've seen before. If we look at them, we'll see that 10 of them have access keys which are older than 90 days. So best practice is to rotate your keys every 90 days since these are static credentials that if discovered, they can be used to abuse the account. We can also see that four of these users have passwords and no MFA set. Now you might notice that most of them do not have password access. That's because we've moved over to using SSO. And these four users unfortunately did not do what was required of them, or they've been handled since then. But they do pose brute force risk to the account just by having passwords with no MFA. And if that's not enough, we also see that two users have not been used for more than half a year, more than 200 days. There's really no reason to keep live access keys that have such a high level of access to the account without them being used at all. So this example shows us two things. One is that it's difficult to enforce patterns over time. You have to manually go through the user list and find the, I would say, the people who are out of line or the resources that are misconfigured. But it's also hard to trace and verify usage. It could be that a person was using this account a lot for a year and has since stopped, but we won't know it unless we check. And we have to check it per user, per role, per entity in IAM. So we have all of these problems which cause layer and layer of garbage and stuff that's left behind and waiting to be now used. And we were wondering how we can do it. So what I developed is called Air IAM. It's completely open source. You can check out the link. I'll share it later too. And what it does is distribute IAM through Terraform. It creates Terraform code from your existing IAM. Let's walk through and see what it does. So before we created the open source, obviously, we had to do a few manual iterations to see what works and what doesn't. And we came to this flow of conclusions and actions. So at first we would like to collect all the IAM entities from the account and their usage using AWS APIs, maybe CloudTrail data and so forth. And then for each entity cross-reference it with its usage and decide whether the entity is in use or not. Whether the entity is in use or not is a question that depends on the threshold for usage. I would say the default best practice is 90 days. I've seen it go down to 30 or up to 365 days. So it really depends on your preference. Once we've decided what's not used, it's easy. We can mark it for division. And we will need to go through the permissions that are in use. So we will have to go through the entities and look at their permissions, look at what they've actually used using Access Analyzer or CloudTrail and create, I would say, a minimized version of the policies that they have based only on stuff that they use, meaning we won't be able to impair any workloads, not dev, not production, not anything. If it is in use, we're leaving it as is. The third one is to create a recommendation. That usually included a very large workbook with a few sheets in it that describes what we want to do for each entity with a few different, I would say, categories. And after we've done that, we'll take that to the relevant account stakeholder or the person who actually uses the account and knows the types of usage in the account. For approval, that might seem like a pretty long process. You actually won't understand how long it is. I've added some time estimations to the different steps. I would say the most difficult part is to create a recommendation and then get the approval from the relevant stakeholders. I've seen it take weeks and it's just a lot of misunderstanding and meetings and getting formal approval. And then I found out that even implementing the changes after I have the action items and I have the approval, it takes me a while. An hour is for a medium-sized account. It could take a lot longer for bigger accounts. So after iterating through this a few times and making sure that we understand what we need, we decided, like we do in many other fields here at Bridgecrew, that we want to automate it. When we wanted to build the automation, we decided that we have four main, I would say, goals we wanted to reach through the automation tool. So the first one is that we want to identify unused entities and easily remove them. When I say unused entities, I also mean unused policy attachments. So that means policies that are not in use at all by the role or user or group that has that policy attached. I want to also remove that. We want to be able to detect drifts and enable rollback. Let's say I made some modification and it caused downtime. For some reason, something went wrong. I can always roll back and make sure everything works. I want to be able to handle scales of thousands of entities. Again, when I say entities, also the relationships between the users and the policies and the roles and the groups. So it very easily reaches the 1,000, 2,000 threshold on big accounts. And then the fourth one, which is very important, is to be able to take the plan to get approval from the relevant stakeholders. But I want something that I can share and people will easily understand and either say that's fine or not. So the tool itself, ARIAM, is pretty easy. All it does is collect data from AWS APIs, both IAM and Access Advisor, cross-references it and either outputs it as CLI output for your convenience or Terraform code, which we'll see in a bit how that, I would say, leverages the power to the maximum. So let's start talking about the tool. ARIAM contains three commands built in. The first one is called Find Unused. What it does is collect all of the data from the APIs and decides whether the entity is used or not. The default setting is 90 days. Let's do a short demo and see how it works. Great. So I've already installed ARIAM on this computer, although it is very easy to install. And I can just run ARIAM Find Unused. So it now gets all the IAM configurations for the account. It will then collect the usage for all the users and the roles in the account. And right after that, it will output all the findings. Awesome. So you can see it's generating the reports, editing it, and now it will start outputting the unused entities that it found in the account. So as you can see, we have here four users who have not been used. It says last used 365 days ago. That's the maximum that AWS promises that there was no usage in. We can also see there's one active access key that isn't being used. You'll notice that it's for a user that's not listed under the unused users. So ARIAM does not give you duplicate results. If the user is unused, it won't say that its access keys are unused, although he might have access keys that are unused. Sorry. We also have one user, that's my user, which has password access to the console with MFA, but hasn't used it for over 100 days. If it doesn't have MFA, it will say it here. So you'll be able to understand who is, I would say, the bigger problem. We also have four roles which are unused. Those are roles that haven't been used for, I'd say, over 100 days. Yes. We have two groups which are redundant. One of them has no members, which means it's just laying there and it's a security hazard because, let's say, it does have any policies attached. If you attach a user to it, it will automatically gain all those permissions. So even it's just leaving behind privileges that maybe an attacker can utilize. And then group without policies. Groups can have members but have no policies attached. And since a group is not an entity that can be used in AWS, just groups' privileges, there's no point in having those groups. And since we want to make AWS IAM as manageable as possible, it also recommends deleting those kinds of groups. We have a redundant policy that's a policy that's not attached to any user, group, or role. It's just clutter that's being left behind in the account. And we also have two policy attachments. For policies that are unused by the relevant principle. So this user, S3User1, is not using any privileges given to him by Amazon API Gateway Administrator. But he does have that privilege given to him, which can cause him to make actions which he shouldn't, such as creating or even deleting existing API gateways. Great. So I hope we've got all of that clear. And this helps us actually create some of the permissions baseline, since it allows us to remove attachments of policies that are unused but given. And create more of a list privilege model for our IAM. The second command is called recommend groups. What it does is try to look through the users that you have in the account, group them according to their relevant privileges, and actually create recommendations for three types of groups currently. One is the admins group, which are active admins. The second one is power users, which are for users who have write access to the account but not admin access. And the third one is the read-only group. That's relevant for all the users who have read access to the account but don't have any write access. This command helps us finalize the permissions baseline or the changes we want to... the things that are in use and create the recommendation, what we want the account to look like in the end. So let's see that as well. I'll hit ARIM, recommend groups. Awesome. So in this account, we have two active users. One is my user, which is an admin and requires admin access. And also this S3 user one, which has write access to S3. We don't have any read users in this account, so it doesn't offer me the read-only. There's no point in creating empty groups as we've talked before, but if there were any, it would show them here. So we have the recommendation, it's just not actionable yet. We have everything we want to do, but we can't show it to anyone and we can't just hit play and make that work. That's where the third command comes in. It's called ARIM Terraform. And what it does is create Terraform code and state file. I'll show you how it works and we'll talk a bit about Terraform so you understand all the power that this command brings with this. So, I'll type in ARIM Terraform and what it does is it will initialize Terraform, it will create Terraform files for me, and then it will import the existing Terraform entities into the local state file. Let's see how that looks. So I'll change to the results there and you'll see that it created just now. This is in my time zone. It created the .terraform directory, which includes all the files that are necessary by Terraform to run. This is a temporary file, so you can ignore it. It creates .tf files that define all the different entities and their relationships using Terraform HCL code. It also creates a state file and the backup, which again, you don't need to care about because it is only temporary and for system usage. Let's take a look at groups.tf. So, what this shows you is the four groups that we have in the account and the policies that they have attached, if any. So, for example, we have the admins group and the admins group has administrator access attached to it. We have a group without members and that group has two policies attached, the RDS full access and the user change password. We also have a group without policies, which has no policies attached, and the S3 group, which has the Amazon S3 full access. Just so we can see it for real, I've opened the console for it and this is the groups in the account. You'll see these are exactly the four groups we have and you can see that the group without policies actually does not have policies and the group without members has these two permissions. So, the same goes for roles. Let's see the roles file, for example. So, for every role, we'll have the role itself. We'll also have the assume role policy document and if it has any policy attachments, such as this one, it will be listed as well. You'll notice that the syntax is not using any hard-coded strings. Basically, ARN creates the entities and connects them so you can actually see who has access to what. Awesome. So, that's all the code. Let's take a look at the state file. So, the way Terraform works is it creates both the code and the state file and the state file describes what connects the entities that were defined in code with the actual ARNs in the cloud environment. So, for example, we have here the AWS IAM group admins as we've seen it in the code and that's connected to this instance with this ARN. So, if I copy it and I go to admins, that's exactly the ARN of the group. So, that means this code describes this group and when I will run Terraform apply or Terraform destroy or Terraform plan, it will go and check what's configured in the code and what's configured in the state file and fix the differences. So, if it needs to delete or create or modify anything that's been described in the code and is not as is in the console, in the actual account, it will be fixed. Awesome. So, once I've created this, I can actually create the Terraform code, if you'd like, without the import stage which takes a while. So, this is what it does and if I type ls-la, you'll see that everything has updated except, well, it just finished now. So, if I run it again, you'll see that everything will change except the state file. See that? Awesome. So, the state file did not change, but all the tf files got updated. So, if I go into the results and type Terraform plan, you'll see that all the differences that it wants to do is unrelated to actual entity modifications. This Terraform code describes the actual entities almost one-to-one. You see it has a force destroy flag added to the users. That's so when you select to destroy a user, it will actually go and destroy, including the access keys, the password access, everything. So, I'll run Terraform apply just to make sure we don't have any differences. You'll see it takes a few seconds. And then we have the Terraform output form and the actual account exactly in line. Hold on. I don't want to break it. Yes. Awesome. So, now let's say I want to delete all the unused entities that I've just found. Instead of going through and deleting one-by-one the policies and the groups and the users and the roles, I can just type here minus minus without unused. Great. So, now it created the Terraform files. But if we'll take a look, for example, at groups.tf, you'll see that we only have two groups now, which are the two groups that both have users and have policies attached. Pretty simple. Now, when I run Terraform plan again, you'll see that it offers to delete everything for me. And it will be much higher than five or ten entities. It actually plans to destroy 40 through users, roles, policies, and all the relationships between them. And it will take just a few seconds and the Terraform will apply. That's all you need. Pretty slick. Awesome. So, we've created a recommendation that's very actionable. I can take it and apply it. But I don't have any way to show it and get approval for it. But another very powerful plus side of using Terraform is that it's code. And for code, we have version control systems. I like GitHub, so these are print screens from GitHub. But basically, what you can do is commit the Terraform code that is outputted by default with everything to a master branch, check out a new branch, run it without unused, for example, and open a PR. And that PR talks in a very simple language that developers can understand, even less technical guys can understand that if there's a group here that's called group without members and has a minus in front of it, it will probably get deleted. So you can use that to actually have all the conversation around getting approval and making everything, I would say, approved or agreed upon. And then, again, just a simple Terraform apply will make all those changes for you. So that completes our entire flow. Although we have three commands to help crunch through all the data and understand the phases, in general, you can just run directly RIM Terraform, it will do all the work for you. It will find all the unused entities, it will import them into Terraform, and you'll be able to actually run it again without unused or with unused, run Terraform apply, everything will be in your hands. Now, you've run it once, that's great. You have your IAM set up clean and sparkling and beautiful. But the problem with the cloud is that well, it's both a plus and a minus, I would say, is that people can manually change and make modifications. Now, using RIM on a weekly basis, monthly basis, on a regular basis, you'll be able to scan your IAM and identify drifts in existing configurations. That means if there were any modifications or deletions, you'll be able to find those things in code and actually automatically update your Terraform state and your Terraform code. You'll also be able to identify changes in usage status. So we've seen the problem where we had admins who have been unused for a long time. If you scan on a regular basis, it will by itself find entities that have been unused for 90 days or if you modify your threshold by your threshold and mark them for deletion and recommend you delete them. It will also help you identify new configurations. So let's say you have a new team or you have a new service or you even started a new Lambda and you created a role for it, that role will automatically be integrated into your Terraform. It will be imported into your state file and you can add it to your repository where you will still have it managed and you'll be able to see it. And nothing will go without knowledge. And then the last thing that ARAM and Terraform enable to you is to use static analysis tools to identify bad practices. So I would like to take this to the next slide actually which shows, I would say, two really great tools that will help you use infrastructure as code and static analysis for your permissions in your IAM. So the first one is called Chekhov. It's also pretty easy to install and I already have it set up here. So Chekhov, what it does is identify bad practices in Terraform. So if I run... let's see... the last thing I did was without the unused right? So I want to do it one second with everything. So right now I created Terraform for the existing IAM setup. And if I go to the results tab, I can just run Chekhov minus the results. This will make Chekhov scan all the Terraform files that are in the results directory. And we'll see in a few seconds that it has quite a lot of findings. So if I scroll up, yes, quite a lot indeed. So it found, it ran 56 checks and 23 of them failed. This includes misconfigured admins and bad assumer role policies and policies that are attached directly to users as this would require justification. Otherwise, it is really recommended to use groups. Let's say I ran it without the unused, just for checking what that would mean. By deleting all the unused entities and running Chekhov again, I will see how much cleaner the account is. And the account is a lot cleaner. So we only have two failed checks and both of them can be either justified or fixed. And all in all, we only have 11 checks that were scanned. That means a lot of the overhead was cleaned simply by deleting unused stuff. Awesome. Another really cool tool is PolicySentry. PolicySentry allows you to build IAM policies from scratch using YAML syntax and easy, simple CRUD syntax. So you have create, read and update and delete syntax for different resources and different services. And that creates an IAM policy that's probably better than what your average developer would create. If you're talking about scanning your existing configurations, I also have a few tools up my sleeve. So the first one, I would say the category is finer and more granular disk privilege for IAM. We have CloudTracker by Geolabs and Ardvark plus RepoKid by Netflix. They both use CloudTrail data, so you need to have CloudTrail read access to analyze the policies and mark specific privileges and break down all the stars and the jokers in your policy to understand what you're giving that isn't really in use. I would say that CloudTrail visibility or coverage is not 100%. Not all actions are by default recorded in CloudTrail. And so using CloudTrail comes with a... maybe you should take it suspiciously. Another really cool tool that I'd like to show is called CloudSplaining. CloudSplaining is another tool by Salesforce that identifies possible risks in your IAM configurations. So it has four different classes, privilege escalation, data exfiltration, other stuff. Let me show you real quick. So I run CloudSplaining. It's a very simple setup again. So what it does is it scans all of the policies that are in the account and it outputs a very detailed and rich report about all of the stuff that you can find in your account. You can actually go by the policies or by the principles, which is what I prefer, and just take a look, for example, who has access to infrastructure modification. So say the misconfigured admin user has... well, since he's a misconfigured admin user, he has all the bad stuff that he can. If we'll take a look at the misconfigured IAM user, you'll see that he has this very bad policy, which we've talked about. It allows everything on everything. So it shows that he can do a lot of infrastructure modifications, do privilege escalation, do resource exposure, and data exfiltration. You can actually go through this by risk and start diving deeper into that. And then the final tool I'd like to show you is called PrincipleMapper or PMapper. It's by NCC Group. It helps identify possible privilege escalations in the account. So if you remember, we had the graph where a user can assume a role and that role can assume another role and each role has its own privilege, I would say policy context of policies. So this tool creates a graph. You see it here. An example, it's a very partial graph. It has these two roles and this user, for example, can assume this role. And so the user, which is called EC2 Manager, can assume the role and gain all the privileges that are attached to EC2 role for SSM. And this allows you to map all the different roles and maybe if you identify a role that is super powerful, check who can assume it and make sure that that's in check. Yep, that's it for the slideshow. All right. I guess to start off with one question for me, I'm assuming that if you wanted to make a change, a specific change instead of the whole IAM recommendation made by AIR IAM, you would just go ahead and edit the Terraform file, right? And so are there any specific config files within AIR IAM that you can use to specify what to detect and what to ignore or anything like that? So Terraform itself allows you to maybe, I would say, create or modify or destroy anything you'd like within the code and the state file. So AIR IAM gives you all of the changes you'd like. You can run Terraform apply as I did in the slideshow, but you can also specify a specific target, be it a user, a group, or a policy, and Terraform will modify only that part of the findings. Got it. And then another one, do you plan on bringing any of these features to GCP or Azure, something along those lines? Definitely. So that's something that we've heard before. We are planning to do this. It requires a complete different set of, I would say, you need to use the other SDKs and it will require more knowledge because GCP IAM is not exactly like AWS IAM. It has a lot more of privileges given by the resources and not the principles, but we are definitely planning to go in that direction. Got it. Another question from Artemis in chat. How well do these tools work in a multi-account environment? That's a great question. So I would start out by saying that IAM permissions are limited to the account. You can give cross-account permissions, but that would be from one account you give the other account permissions. So the context of every, I would say, analysis is a specific AWS account. So you can use ARAM using all the default AWS credentials tools. You can use the profile and specify the minus minus profile flag. You can use AWS environment variables to scan different accounts, but every ARAM scan is limited to the context of one account since that is the context of IAM."
}