What is AWS Lambda Runtime?

Amazon Web Services (AWS) Lambda runtimes are more than just an execution environment — they shape how applications scale, integrate, and remain secure in a serverless architecture. While developers often view them as technical enablers for event-driven workloads, runtimes come with broader impacts, influencing outcomes like operational agility and security posture. We’ve discussed AWS Lambda security. Now we’re exploring the role of AWS Lambda runtimes in serverless architecture.

What is AWS Lambda Runtime?

AWS Lambda Runtime is the execution environment for AWS Lambda. It enables Lambda functions to process events, acting as the bridge between developers’ function code and the AWS Lambda service.

AWS Lambda runtime features include:

• Programming Language Support: Runtimes provide compatibility with supported languages like Python, Node.js, Java, and more.
• Runtime API: Facilitates communication between the Lambda function and the AWS infrastructure, managing function requests and responses.
• Custom Runtimes: Allows users to bring their own runtime for non-supported languages, using the Runtime API and Lambda Layers for customization.

AWS Lambda Runtime enhances performance, reducing cold start delays (initialization lags for first-time invocations) and optimizing execution speed. However, while the runtime environment is inherently secure as part of AWS’s managed infrastructure, it introduces potential risks at the function level, such as dependency vulnerabilities and misconfigurations. This makes runtime security a shared responsibility, requiring developers to ensure proper configurations and secure dependencies for maintaining function integrity.

Understanding AWS Lambda runtimes is the first step toward leveraging their capabilities while mitigating operational and security challenges.

Benefits and Challenges of AWS Lambda Runtimes

As serverless adoption grows, the runtime has become more than a functional component — it’s now a key factor in shaping application performance, scalability, and security posture. The rapid shift toward event-driven architectures introduces unique trade-offs, as organizations grapple with balancing runtime flexibility against the need for strong defenses in increasingly complex serverless environments.

“Threats are evolving faster than ever, and organizations need tools that don’t just detect issues but help them act decisively. The complexity of serverless environments like Lambda makes this even more critical.”

-Joshua Burgin I CPO, Upwind

This changing landscape underscores the importance of understanding both the advantages and risks of AWS Lambda runtimes. Let’s break down the most significant ones.

Benefit: Simplified Multilingual Support for Rapid Development

AWS Lambda runtimes support multiple programming languages, enabling developers to write functions in their preferred language (e.g., Python, JavaScript, or Go). Custom runtimes expand this flexibility to niche or experimental languages. 

However, with multilingual support comes the risk of importing vulnerable libraries or dependencies. Observability into these layers is critical.

Benefit: Automatic Scaling with Event-Driven Architecture

Runtimes let Lambda functions scale automatically based on incoming requests so performance stays consistent during traffic spikes.

However, scaling exposes functions to an increased risk of misconfigurations, especially when handling sudden API request surges.

Challenge: Cold Start Delays Can Impact User Experience

AWS Lambda runtime comes with drawbacks as well as benefits. In this case, runtimes require initialization during “cold starts,” which can slow the response for infrequently triggered functions.

Cold starts also reinitialize environments, increasing the chance of temporary misconfigurations or exposed variables.

Challenge: Dependency Vulnerabilities Can Exist in Runtime Layers

Lambda layers that share dependencies between functions may include outdated or vulnerable libraries.

Continuous monitoring ensures runtime layers remain secure and up-to-date. Leveraging AWS tools like CloudWatch, CloudTrail, and Lambda’s execution logs to detect anomalies or potential threats during runtime can help. 

Challenge: Limited Visibility Without Observability Tools

AWS Lambda’s serverless architecture abstracts the underlying infrastructure, which limits the runtime-level insights available through native AWS tools. While services like CloudWatch and CloudTrail provide basic logging and monitoring, they don’t offer detailed visibility into the function’s execution environment. This lack of granularity can make it difficult to:

• Detect anomalies, such as unexpected behavior during function execution.
• Trace root causes of performance or security issues, especially in complex workloads.
• Identify misconfigurations or vulnerabilities in real time.

How AWS Lambda Runtime Compares to Other Services

AWS Lambda runtime has changed developers’ approach to serverless computing, but it’s not the only option. Let’s compare AWS Lambda runtimes to other cloud and open-source solutions to explore differences in management, scaling, and use cases.

Service	Provider	Primary Use Case	Management Level	Scaling	Granularity
AWS Lambda Runtime	Amazon Web Services	Event-driven function execution	Fully managed	Automatic per function	Individual functions
Google Cloud Functions	Google Cloud	Event-drive serverless execution	Fully managed	Automatic per function	Individual functions
Azure Functions	Microsoft Azure	Event-drive serverless execution	Fully managed	Automatic per function	Individual functions
OpenFaaS	Open Source	Serverless function orchestration	User-managed	User-defined scaling	Individual functions
Knative	Open Source (K8s)	Serverless with Kubernetes integration	Partially managed	Kubernetes-managed auto-scaling	Containerized services

AWS Lambda, Google Cloud Functions, and Azure Functions all provide fully managed, event-driven serverless computing, offering similar benefits like automatic scaling and function-level granularity. However, minor differences emerge in their ecosystem integrations: 

• AWS Lambda: It integrates tightly with AWS services like DynamoDB (databases), S3 (storage), and API Gateway, making it a good choice for applications already leveraging the AWS ecosystem.
• Google Cloud Functions: This service works seamlessly with Google-specific tools like Firebase (a backend-as-a-service for mobile and web apps) and BigQuery (a data warehousing and analytics platform), making it the choice of applications that rely on these services.
• Azure Functions: Designed to integrate smoothly with Microsoft’s ecosystem, from Microsoft 365 (Office apps, SharePoint, etc.) to Azure-specific services.

So, while the core functionality of these serverless platforms is similar, the deeper value of using serverless platforms like AWS Lambda, Google Cloud Functions, or Azure Functions within their ecosystems lies in streamlined security management, reduced operational complexity, and enhanced observability. 

Here’s a deeper look at ecosystem-specific features:

Shared Security Controls: Each platform leverages its ecosystem’s native identity, access management, and compliance features. For example:

• AWS Lambda benefits from AWS IAM for granular role-based access and integrates with AWS Shield and WAF for runtime protection.
• Google Cloud Functions ties directly into Google Cloud IAM and supports VPC Service Controls to define secure perimeters for sensitive data.
• Azure Functions inherits Microsoft’s strong identity controls via Azure Active Directory. It also integrates natively with Defender for Cloud to detect vulnerabilities.

Integrated Monitoring and Logging: Serverless platforms are built to work with their ecosystem’s observability tools for centralized monitoring:

• AWS Lambda uses CloudWatch for logging and tracing, which offers visibility into runtime behavior.
• Google Cloud Functions integrates with Cloud Logging and Cloud Monitoring for event tracking.
• Azure Functions can use Azure Monitor for logs and alerts.

Ecosystem-Specific Threat Context: Using a platform within its ecosystem means that threat detection and response tools are tailored for that environment, though none can extend that security to a multi-cloud or hybrid environment. For example:

• AWS Lambda with AWS GuardDuty provides real-time threat detection based on AWS-specific activity patterns.
• Google Cloud Functions can be combined with Chronicle (Google’s security analytics platform) for advanced threat hunting.
• Azure Functions works with Sentinel, Microsoft’s Security Information and Event Management (SIEM) solution for incident detection and response.

By choosing a serverless platform native to your cloud ecosystem, you reduce the need for third-party integrations that can introduce vulnerabilities and operational delays. But no ecosystem can cover all the needs of organizations that want to use multiple cloud services (which accounts for ~90% of organizations).

Further, AWS Lambda runtime is just one of several compute services offered by Amazon, each tailored to different workload needs. Comparing Lambda with EC2, ECS, and App Runner highlights some unique advantages of serverless function execution over traditional or containerized options.

Service	Primary Use Case	Management Level	Scaling	Granularity
AWS Lambda Runtime	Event-driven function execution	Fully managed	Automatic per function	Individual functions
Amazon EC2	Full application or OS workloads	User-managed (servers, updates)	User-configured auto-scaling	Virtual servers
Amazon ECS/EKS	Containerized application orchestration	Partially managed (containers)	User-configured container scaling	Containerized microservices
AWS App Runner	Simple containerized web apps and APIs	Fully managed	Automatic per application	Entire application

Within Amazon, selecting the right compute service depends on the specific needs of the workload. 

AWS Lambda runtime excels in event-driven, short-lived tasks where development teams want to avoid managing infrastructure entirely. Use it for applications like processing real-time data streams, responding to API requests, or running automation scripts.

Amazon EC2 is better for workloads requiring full control over the operating system or persistent applications that don’t fit into a serverless framework. For containerized microservices or applications with consistent workloads, Amazon ECS and EKS provide strong orchestration options. 

AWS App Runner offers both simplicity and flexibility, offering automatic scaling and container support for developers who want a straightforward way to deploy web applications.

The choice also impacts operational complexity and security. Lambda’s fully managed runtime minimizes maintenance but requires vigilance around runtime vulnerabilities, such as unpatched dependencies or misconfigured APIs. EC2 offers more control but increases the burden of patching and monitoring. ECS and EKS require careful orchestration of containerized workloads, and finally, App Runner simplifies deployment at the cost of less granular runtime visibility. 

Which is right? There’s no definitive answer. Teams will need to balance these factors in order to keep all workloads efficient, scalable, and secure.

Understanding Runtime Attack Surfaces in AWS Lambda

While AWS Lambda simplifies application deployment, its architecture introduces new attack surfaces that need consideration. That’s because runtimes, as the bridge between code and the AWS infrastructure, can expose vulnerabilities that attackers exploit to compromise functions or underlying systems. These key attack surfaces highlight areas where traditional security practices might fall short in serverless environments:

Event Input Injection

Lambda functions often process unvalidated event inputs from triggers like S3 uploads, API Gateway requests, or DynamoDB streams. Malicious inputs can exploit vulnerabilities, leading to injection attacks like SQL injection, command injection, or deserialization exploits. For instance, a poorly sanitized API Gateway payload could pass harmful input directly into a backend Lambda function, enabling unauthorized access or data exfiltration.

Dependency Vulnerabilities

Lambda functions rely on third-party libraries and custom runtime layers, which may contain known vulnerabilities. Third-party libraries and Lambda Layers are essential for quick development, but they can become a blind spot in vulnerability management. Without proactive scanning and updates, these dependencies can become attack vectors.

Misconfigured Environment Variables

Environment variables in Lambda store sensitive data like API keys or database credentials. But that means that misconfigurations, such as storing secrets in plaintext or over-permissive IAM roles, can lead to privilege escalation or data theft. And those attacks can be particularly damaging due to the potentially broad access Lambda functions have within the AWS environment.

Cold Start Exploits

The initialization phase during a cold start creates a temporary window where misconfigured permissions or unpatched runtime environments could be exploited. For instance, an attacker could inject code during initialization by exploiting an unpatched library in a custom runtime. Any misstep during this phase can be exploited by attackers, especially if runtime isolation or dependency updates are inadequate.

Best Practices to Mitigate Runtime Vulnerabilities

These attack surfaces represent not just technical vulnerabilities but strategic risks to operational integrity. Mitigating these requires implementing best practices:

1. Input Validation and Sanitization: Implement strict validation for all event inputs to prevent injection attacks. Use libraries or frameworks that sanitize inputs automatically.
2. Dependency Management: Regularly scan Lambda functions and layers for vulnerable dependencies. Replace outdated libraries and automate patching where possible.
3. Environment Variable Security: Use AWS Secrets Manager or Parameter Store to manage sensitive data securely. Apply the principle of least privilege to IAM roles associated with your functions.
4. Runtime Observability: Employ tools that provide visibility into runtime behavior, detecting anomalies and monitoring for unauthorized access or execution patterns.

Upwind was Built for Cloud Environments like Lambda

Upwind is a comprehensive CNAPP that enhances AWS Lambda security by providing comprehensive, agentless protection through its cloud scanners. These tools offer real-time monitoring and proactive risk analysis and proactive risk analysis, delivering visibility into serverless functions like AWS Lambda and across your entire cloud infrastructure. 

With Upwind, organizations can effectively secure their AWS Lambda functions without deploying additional sensors, simplifying the security management of serverless architectures. Want to see how? Schedule a demo.

FAQ

What is a custom AWS Lambda runtime, and when should I use one?

A custom AWS Lambda runtime allows you to execute functions in programming languages not natively supported by AWS Lambda. With the Runtime API, developers can define their own execution environment and include any libraries or frameworks required for their applications. Custom runtimes rely on a custom implementation of the Runtime Interface Client (RIC), which communicates with the Lambda service to handle requests and responses.

When should I use a custom runtime?

You should use a custom runtime in AWS Lambda when your application requires features or capabilities that the default AWS-provided runtimes don’t support. However, custom runtimes require more effort to create, test, and maintain than AWS-provided runtimes. They’re not for everybody.

You might choose a custom runtime if:

• You need to run functions in languages not officially supported by AWS Lambda (e.g., C++, Erlang, or Rust).
• Your function relies on specific frameworks, tools, or libraries unavailable in native Lambda runtimes.
• You require a runtime tailored for unique workloads, such as a highly optimized runtime for machine learning inference or data processing.
• You are migrating an application to AWS Lambda and need to replicate a specific runtime environment.

What happens during an AWS Lambda cold start?

An AWS Lambda cold start occurs when a function is invoked for the first time or after a period of inactivity when AWS needs to initialize the execution environment. There are multiple steps to a cold start:

1. Provisioning the Environment: AWS allocates a new execution environment, including the required compute, memory, and networking resources.
2. Loading the Runtime: The runtime (e.g., Python, Node.js, or a custom runtime) is initialized.
3. Initializing the Function Code: AWS loads the function code into the runtime, including any dependencies and libraries. Lambda Layers are also added.
4. Running the Initialization Code: The init phase runs any setup tasks defined in the function code, like establishing database connections.

Cold starts can add latency to function execution. Further, during initialization, misconfigurations or unpatched dependencies can be exposed temporarily. Cold starts are an inherent aspect of AWS Lambda’s serverless design, but teams need to optimize deployment package sizes, runtime configurations, and initialization code to minimize their impacts.

How do Lambda runtimes handle scaling during high traffic?

AWS Lambda runtimes are designed to handle automatic scaling seamlessly during high traffic by creating new instances of the function to process incoming events in parallel. Here’s what the process looks like:

1. Event-Driven Invocation: Each event triggers a new invocation of the Lambda function. If multiple events occur simultaneously, AWS spawns additional execution environments to handle the load.
2. Concurrency Management: Lambda scales by increasing the number of function instances (concurrent executions) to meet demand.
3. Load Distribution: AWS manages the distribution of events across available instances, ensuring balanced performance.
4. Cold and Warm Starts: For new function instances, AWS provisions and initializes the runtime. For later requests, it employs previously initialized environments. 

AWS Lambda runtimes are particularly adept at handling scaling without manual intervention. That makes them ideal for applications with fluctuating or unpredictable traffic patterns.

Can AWS Lambda Runtimes be exploited? What are the common vulnerabilities?

Yes, AWS Lambda runtimes can be exploited if misconfigured or if vulnerabilities exist in the function code, dependencies, or runtime environment. Although AWS secures the underlying infrastructure, users must secure function code, configuration, and dependencies. Common vulnerabilities include:

• Injection attacks
• Dependency vulnerabilities
• Exposed environment variables
• Over-permissioned IAM roles
• Insufficient runtime isolation
• Insecure runtime updates

How do Lambda Layers relate to runtimes?

Lambda Layers let developers manage and reuse shared code, libraries, and configuration settings across multiple functions. Instead of duplicating this code within each function, developers can package it into a layer and reference it when needed.

Layers are tightly integrated with runtimes because they extend the runtime environment by providing additional functionality — like external libraries, frameworks, or binaries — without embedding this code directly in the function package. That keeps function packages smaller. It simplifies updates, too, as changes to a layer automatically propagate to all functions that use it.

Lambda Layers:

1. Augment the Runtime Environment: Lambda Layers are included in the runtime environment of a function at execution time. They allow you to package libraries, frameworks, or shared utilities that the function code can reference during execution.
2. Help customize Runtimes: Layers are often used to customize AWS-provided runtimes or extend the functionality of custom runtimes. 
3. Simplify Code Management: Instead of packaging dependencies directly into the function’s deployment package, developers can place them in a layer. This reduces deployment package size and simplifies updates by allowing shared libraries to be maintained separately.

By integrating Lambda Layers with runtimes, teams can build more modular and maintainable serverless applications while reducing redundancy and deployment complexity.