AWS Compute Blog

Automating chaos experiments with AWS Fault Injection Service and AWS Lambda

This post is written by André Stoll, Solution Architect.

Chaos engineering is a popular practice for building confidence in system resilience. However, many existing tools assume the ability to alter infrastructure configurations, and cannot be easily applied to the serverless application paradigm. Due to the stateless, ephemeral, and distributed nature of serverless architectures, you must evolve the traditional technique when running chaos experiments on these systems.

This blog post explains a technique for running chaos engineering experiments on AWS Lambda functions. The approach uses Lambda extensions to induce failures in a runtime-agnostic way requiring no function code changes. It shows how you can use the AWS Fault Injection Service (FIS) to automate and manage chaos experiments across different Lambda functions to provide a reusable testing method.


Chaos experiments are commonly applied to cloud applications to uncover latent issues and prevent service disruptions. IT teams use chaos experiments to build confidence in the robustness of their systems. However, the traditional methods used in server-based chaos engineering do not easily translate to the serverless world since many existing tools are based on altering the underlying infrastructure configurations, such as cluster nodes or server instances of your applications.

In serverless applications, AWS handles the undifferentiated heavy lifting of managing infrastructure, so you can focus on delivering business value. But this also means that engineering teams have limited control over the infrastructure, and must rely on application-level tooling to run chaos experiments. Two techniques commonly used in the serverless community for conducting chaos experiments on Lambda functions are modifying the function configuration or using runtime-specific libraries.

Changing the configuration of a Lambda function allows you to induce rudimentary failures. For example, you can set the reserved concurrency of a Lambda function to simulate invocation throttling. Alternatively, you might change the function execution role permissions or the function policy to simulate IAM access denial. These types of failures are easy to implement, but the range of possible fault injection types is limited.

The other technique—injecting chaos into Lambda functions through purpose-built, runtime-specific libraries—is more flexible. There are various open-source libraries that allow you to inject failures, such as added latency, exceptions, or disk exhaustion. Examples of such libraries are Python’s chaos_lambda and failure-lambda for Node.js. The downside is that you must change the function code for every function you want to run chaos experiments on. In addition, those libraries are runtime-specific and each library comes with a set of different capabilities and configurations. This reduces the reusability of your chaos experiments across Lambda functions implemented in different languages.

Injecting chaos using Lambda extensions

Implementing chaos experiments using Lambda extensions allows you to address all of the previous concerns. Lambda extensions augment your functions by adding functionality, such as capturing diagnostic information or automatically instrumenting your code. You can integrate your preferred monitoring, observability, or security tooling deeply into the Lambda environment without complex installation or configuration management. Lambda extensions are generally packaged as Lambda layers and run as a separate process in the Lambda execution environment. You may use extensions from AWS, AWS Lambda partners, or build your own custom functionality.

With Lambda extensions, you can implement a chaos extension to inject the desired failures into your Lambda environments. This chaos extension uses the Runtime API proxy pattern that enables you to hook into the function invocation request and response lifecycle. Lambda runtimes use the Lambda Runtime API to retrieve the next incoming event to be processed by the function handler and return the handler response to the Lambda service.

The Runtime API HTTP endpoint is available within the Lambda execution environment. Runtimes get the API endpoint from the environment variable AWS_LAMBDA_RUNTIME_API. During the initialization of the execution environment, you can modify the runtime startup behavior. This lets you change the value of AWS_LAMBDA_RUNTIME_API to the port the chaos extension process is listening on. Now, all requests to the Runtime API go through the chaos extension proxy. You can use this workflow for blocking malicious events, auditing payloads, or injecting failures.

Injecting chaos using Lambda extensions

  1. The chaos extension intercepts incoming events and outbound responses, and injects failures according to the chaos experiment configuration.
  2. The extension accesses environment variables to read the chaos experiment configuration.
  3. A wrapper script configures the runtime to proxy requests through the chaos extension.

When intercepting incoming events and outbound responses to the Lambda Runtime API, you can simulate failures such as introducing artificial delay or generate an error response to return to the Lambda service. This workflow adds latency to your function calls:


All Lambda runtimes support extensions. Since extensions run as a separate process, you can implement them in a language other than the function code. AWS recommends you implement extensions using a programming language that compiles to a binary executable, such as Golang or Rust. This allows you to use the extension with any Lambda runtime.

Some of the open source projects following this technique are the chaos-lambda-extension, implemented in Rust, or the serverless-chaos-extension, implemented in Python.

Extensions provide you with a flexible and reusable method to run your chaos experiments on Lambda functions. You can reuse the chaos extension for all runtimes without having to change function code. Add the extension to any Lambda function where you want to run chaos experiments.

Automating with AWS FIS experiment templates

According to the Principles of Chaos Engineering, you should “automate your experiments to run continuously”. To achieve this, you can use the AWS Fault Injection Service (FIS).

This service allows you to generate reusable experiment templates. The template specifies the targets and the actions to run on them during the experiment, and an optional stop condition that prevents the experiment from going out of bounds. You can also execute AWS Systems Manager Automation runbooks which support custom fault types. You can write your own custom Systems Manager documents to define the individual steps involved in the automation. To carry out the actions of the experiment, you define scripts in the document to manage your Lambda function and set it up for the chaos experiment.

To use the chaos extension for your serverless chaos experiments:

  1. Set up the Lambda function for the experiment. Add the chaos extension as a layer and configure the experiment, for example, by adding environment variables specifying the fault type and its corresponding value.
  2. Pause the automation and conduct the experiment. To do this, use the aws:sleep automation action. During this period, you conduct the experiment, measure and observe the outcome.
  3. Clean up the experiment. The script removes the layer again and also resets the environment variables.

Running your first serverless chaos experiment

This sample repository provides you with the necessary code to run your first serverless chaos experiment in AWS. The experiment uses the chaos-lambda-extension extension to inject chaos.

The sample deploys the AWS FIS experiment template, the necessary SSM Automation runbooks including the IAM role used by the runbook to configure the Lambda functions. The sample also provisions a Lambda function for testing and an Amazon CloudWatch alarm used to roll back the experiment.


Running the experiment

Follow the steps outlined in the repository to conduct your first experiment. Starting the experiment triggers the automation execution.

Actions summary

This automation includes adding the extension and configuring the experiment, pausing the execution and observing the system and reverting all changes to the initial state.

Executed steps

If you invoke the targeted Lambda function during the second step, failures (in this case, artificial latency) are simulated.

Output result

Security best practices

Extensions run within the same execution environment as the function, so they have the same level of access to resources such as file system, networking, and environment variables. IAM permissions assigned to the function are shared with extensions. AWS recommends you assign the least required privileges to your functions.

Always install extensions from a trusted source only. Use Infrastructure as Code (IaC) and automation tools, such as CloudFormation or AWS Systems Manager, to simplify attaching the same extension configuration, including AWS Identity and Access Management (IAM) permissions, to multiple functions. IaC and automation tools allow you to have an audit record of extensions and versions used previously.

When building extensions, do not log sensitive data. Sanitize payloads and metadata before logging or persisting them for audit purposes.


This blog post details how to run chaos experiments for serverless applications built using Lambda. The described approach uses Lambda extension to inject faults into the execution environment. This allows you to use the same method regardless of runtime or configuration of the Lambda function.

To automate and successfully conduct the experiment, you can use the AWS Fault Injection Service. By creating an experiment template, you can specify the actions to run on the defined targets, such as adding the extension during the experiment. Since the extension can be used for any runtime, you can reuse the experiment template to inject failures into different Lambda functions.

Visit this repository to deploy your first serverless chaos experiment, or watch this video guide for learning more about building extensions. Explore the AWS FIS documentation to learn how to create your own experiments.

For more serverless learning resources, visit Serverless Land.