Let's look at AWS Access Keys inside a Lambda function, from how they are populated into the function's execution context, how long they last, how to exfiltrate them out and use them, and how we might detect an compromised access keys.

But before that, let's go through some basics. Lambda functions run on Firecracker, a microVM technology developed by Amazon. MicroVMs are like docker containers, but provide VM level isolation between instances. But because we're not going to cover container breakouts here, for the purpose of this post we'll use the term container to refer to these microVMs.

Anyway...

Lambda constantly spins up containers to respond to events, such as http calls via API Gateway, a file landing in an S3 bucket, or even an invoke command executed from your aws-cli.

These containers interact with AWS services in the same exact way as any code in EC2, Fargate or even your local machine -- i.e. they use a version of the AWS SDK (e.g. boto3) and authenticate with IAM access keys. There isn't any magic here, it's just with serverless we can remain blissfully ignorant of the underlying mechanism.

But occasionally it's a good idea to dig deep and try to understand what goes on under the hood, and that's what this post seeks to do.

So where in the container are the access keys stored? Well, we know that AWS SDKs reference credentials in 3 places:

• Environment Variables
• The ~/.aws/credentials file
• The Instance Metadata Service (IMDS)

If we check, we'll find that our IAM access keys for lambda functions are stored in the environment variables of the execution context, namely:

• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_SESSION_TOKEN

You can easily verify this, by printing out those environment variables in your runtime (e.g. $AWS_ACCESS_KEY_ID) and see for yourself.

OK, now we know where the access stored keys are stored, but how did they end up here and what kind of access keys are they? For that, we need to look at the life-cycle of a Lambda function...

Potassium40 was a project I started to see how fast Lambda could really go. The project attempts to download the robots.txt files from 1 million websites as fast as it can. I chose robots file because -- well it's supposed to be downloaded by robots anyway, so this was both great fun, but also completely ethical as I wasn't scraping people's websites.

This post covers how to perform logging within AWS Lambda. Lambda has built-in integration to Cloudwatch logs, making it a default choice for logs, but the way a distributed system like lambda performs logging, is quite different from how you'd do in a monolithic app.

For the brave folks still reading this -- let's dive in.

Lambda Logging: 101

When building lambda functions, it's always a good idea to sprinkle logging statements throughout your code -- particularly in areas where something important happens. This is just generally good advice regardless of whether you're using Lambda.

A lambda function is a like a little island, surrounded by network. Unlike Fargate containers, of EC2 instances, they do not have EFS, EBS or some other fast storage support. Everything that goes into a lambda, goes in via the network interface (and network only).

Last week I took the AWS Certified Security - Specialty exam -- and I passed with a score of 930 (Woohoo!!)

In this post I cover why I took it, what I did to pass, my overall exam experience, and some tips I learnt along the way.

So let's go.

Why?

Why would anybody pay good money, subject themselves to hours of studying, only to end up sitting in a cold exam room for hours answering many multiple choice questions!

And the reward for that work is an unsigned PDF file claiming you're 'certified', and 'privilege' access to buy AWS branded notebooks and water bottles!! Unless those water bottles come with a reserved instance for Microsoft SQL server in Bahrain, I'm not interested.

But, jokes asides, I did this for fun and profit, and fortunately I really did enjoy the preparing for this exam. It exposed me to AWS services that I barely knew -- and forced me to level-up my skills even on those that I knew.

The exam has a massive focus on VPC, KMS, IAM, S3, EC2, Cloudtrail and Cloudwatch. While lightly touching Guardduty, Macie, Config, Inspector, Lambda, Cloudfront, WAF, System Manager and AWS Shield.

You need to catch you breath just reading through that list!

But for those diligently keeping count -- you'd notice that the majority of those services are serverless -- meaning the exam combined my two technological love-affairs ... security and serverless!

I wasn't lying when I said it was fun. So what about the profit.

I'm not sure how good this would be for my career (I literally got the cert last week), but for $300, it's is relatively cheap, with a tonne of practical value. So trying to get an ROI on this, isn't going to be hard.

For comparison, the CCSP certification cost nearly twice as much, is highly theoretical and requires professional experience.

The results also help me validate my past years of working on serverless projects, proving I wasn't just some rando posting useless hobby projects on GitHub. Instead, I'm now a certified AWS professional, posting useless hobby projects on GitHub (it's all about how you market it!)

So now that we've covered the why, let's move onto how.

GitHub actions is the new kid on the workflow block.

It allows users to orchestrate workflows using familiar git commands like push & pull requests, and un-familiar GitHub events like gollum, issue creation and milestone closures.

In this post, we'll use GitHub actions to orchestrate a build pipeline that will deploy lambda functions using the Serverless framework. There's a lot of tutorials that cover the basics of doing this, but we'll dive deeper and cover off framework plugins and deploying to different environments using feature branches.

In my honest (and truly humble) opinion, VPCs don't make much sense in a serverless architecture -- it's not that they don't add value, it's that the value the add isn't worth the complexity you incur.

After all, you can't log into a lambda function, there are no inward connections allowed. And it isn't a persistent environment, some functions may timeout after just 2-3 seconds. Sure, network level security is still worthy pursuit, but for serverless, tightly managing IAM roles and looking after your software supply chain for vulnerabilities would be better value for your money.

But if you've got a fleet of EC2s already deployed in a VPC, and your Lambda function needs access them. Then you have no choice but to deploy that function in a VPC as well. Or, if your org requires full network logging of all your workloads, then you'll also need VPC (and their flow logs) to comply with such requests.

Don't get me wrong, there is value in having your functions in a VPC, just probably not as much as you think.

Put that aside though, let's dive into the wonderful world of Lambda functions and VPCs

Working Example

First, imagine we deploy a simple VPC with 4 subnets.

1. A Public Subnet with a Nat Gateway inside it.
2. A Private Subnet which routes all traffic through that NAT Gateway
3. A Private Subnet without internet (only local routing)
4. A Private Subnet without internet but with a SSM VPCe inside it

Let's label these subnets (1), (2) ,(3) and (4) for simplicity.

Now we write some Lambda functions, and deploy each of them to each subnet. The functions have an attached security group that allows all outgoing connections, and similarly each subnet has very liberal NACLs that allow incoming and outgoing connections.

Then we create a gateway S3 VPC-endpoint (VPCe), and route subnet (4) to it.

Finally, we enable private DNS on the entire VPC. And then outside the subnet we create a bucket and an System Manager Parameter Store Parameter (AWS really need better terms for these things).

The final network looks like this:

Amazon KMS is one of the most integrated AWS services, but probably also the least understood. Most developers know about it, and what it can do, but never really fully realize the potential of the service. So here's a rundown of the innards of the KMS service.

What is KMS?

KMS (Key Management Service) is an AWS offering that allows us to create, manage and use cryptographic keys. Like everything else in AWS, it's highly available, provided via API, and charged on a per-use basis.

Had a blast at PyConSG 2019, really cool to be in the presence of so many pythonistas. Would definitely recommend, especially since python is one of the more broadly used languages (AI, Blockchain, RPA, etc).

My talk was on AWS Lambda (naturally!).

Slide deck can be found in this GitHub Repo. Let me know what you think either in the comments here or on Youtube.

Everyone knows that I'm a Lambda fanboy, and to be fair Lambda deserves all the praise it gets, it is **the** gold-standard for serverless functions. But yesterday, I gave Google Cloudrun a spin, and boy(!) is Lambda is going to get a run for its money.

Which is surprising given Google has traditionally lagged in this area -- isn't it quaint that we use words like 'traditional' in the serverless world!

But I digress.

The Lambda equivalent in the Google world, is Google cloud functions ... which is (generously speaking) what lambda was 2 years ago-- pretty boring. The only advantage I saw it having over Lambda, was the ability to build python packages natively in the requirements.txt file. But that incurred a build during deploy, which in turn had a limit.

And while, it did allow for a larger package size (double what AWS Lambda offers) it was severely more complex to understand. Just looking at it's limit and pricing models can make you dizzy.

In short, Google Cloud functions lacked the simplicity of Lambda, with little benefit for incurring all that additional complexity.

But Cloud Run is something else. It's still more complex than lambda, but here the trade-off seems worth it. So let's take a peek at Google's new serverless Golden Boy!

Containers vs. Functions

In Lambda the atomic unit of compute is the function, which for an interpreted language like Python is just plaintext code uploaded to AWS. But in Cloud Run the atomic unit is the container -- and that can be a container for just the one function, or the container for the entire app itself -- with all the routing logic embedded within it.

Now why would you need apps for the serverless world?! You ask indignantly. Aren't these all supposed to be function based?

Well actually lots of people have legacy code written at the application level, and re-writing an entire application takes a long time, and very rarely succeeds on the first try.