Challenges of Securing Microservices

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Challenges of Securing Microservices

An entry point for an application is analogous to a door in a building. Just as a door lets you into a building, an entry point lets your requests in.

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In the 1st chapter of the book, Microservices Security in Action, which I authored with Nuwan Dias , we list out a set of key challenges in securing microservices, and then throughout the book see how we can methodically address them.

A monolithic application has few entry points. An entry point for an application is analogous to a door in a building. Just as a door lets you into a building (possibly after security screening), an application entry point lets your requests in.

Think about a web application running on the default HTTP port 80 on a server carrying the IP address Port 80 on server is an entry point to that web application. If the same web application accepts HTTPS requests on the same server on port 443, you have another entry point. When you have more entry points, you have more places to worry about securing. (You need to deploy more soldiers when you have a longer border to protect, for example, or to build a wall that closes all entry points.) The more entry points to an application, the broader the attack surface is.

A monolithic application typically has few entry points. Here, there are two: ports 80 and 443

Most monolithic applications have only a couple of entry points. Not every component of a monolithic application is exposed to the outside world and accepts requests directly.

In a typical Java EE web application such as the one in the above figure, all requests are scanned for security at the application level by a servlet filter.This security screening checks whether the current request is associated with a valid web session and, if not, challenges the requesting party to authenticate first.

Further access-control checks may validate that the requesting party has the necessary permissions to do what they intend to do. The servlet filter (the interceptor) carries out such checks centrally to make sure that only legitimate requests are dispatched to the corresponding components. Internal components need not worry about the legitimacy of the requests; they can rightly assume that if a request lands there, all the security checks have already been done.

In case those components need to know who the requesting party (or user) is or to find other information related to the user, such information can be retrieved from the web session, which is shared among all the components. The servlet filter injects the requesting-party information into the web session during the initial screening process, after completing authentication and authorization.

Multiple entry points (ports 80 and 443) are funneled to a single servlet filter. The filter acts as a centralized policy enforcement point.

Once a request is inside the application layer, you don’t need to worry about security when one component talks to another. When the Order Processing component talks to the Inventory component, for example, you don’t necessarily need to enforce any additional security checks (but, of course, you can if you need to enforce more granular access-control checks at the component level). These are in-process calls and in most cases are hard for a third party to intercept.

In most monolithic applications, security is enforced centrally, and individual components need not worry about carrying out additional checks unless there is a desperate requirement to do so. As a result, the security model of a monolithic application is much more straightforward than that of an application built around microservices architecture.

Mostly because of the inherent nature of microservices architecture, security is challenging. In this blog, we discuss the challenges of securing microservices without discussing in detail how to overcome them. In the book, we discuss multiple ways to address these challenges.

The Broader the Attack Surface, the Higher the Risk of Attack

In a monolithic application, communication among internal components happens within a single process — in a Java application, for example, within the same Java Virtual Machine (JVM). Under microservices architecture, those internal components are designed as separate, independent microservices, and those in-process calls among internal components become remote calls. Also, each microservice now independently accepts requests or has its own entry points.

As opposed to a monolithic application with few entry points, a microservices-based application has many entry points that all must be secured.

Instead of a couple of entry points, as in a monolithic application, now you have a large number of entry points. As the number of entry points to the system increases, the attack surface broadens too. This situation is one of the fundamental challenges in building a security design for microservices. Each entry point to each microservice must be protected with equal strength. The security of a system is no stronger than the strength of its weakest link.

Distributed Security Screening May Result in Poor Performance

Unlike in a monolithic application, each microservice in a microservices deployment has to carry out independent security screening. From the viewpoint of a monolithic application, in which the security screening is done once and the request is dispatched to the corresponding component, having multiple security screenings at the entry point of each microservice seems redundant. Also, while validating requests at each microservice, you may need to connect to a remote security token service (STS). These repetitive, distributed security checks and remote connections could contribute heavily to latency and considerably degrade the performance of the system.

Some do work around this by simply trusting the network and avoiding security checks at each and every microservice. Over time, trust-the-network has become an antipattern, and the industry is moving toward zero-trust networking principles. With zero-trust networking principles, you carry out security much closer to each resource in your network. Any microservices security design must take overall performance into consideration and must take precautions to address any drawbacks.

Deployment Complexities Make Bootstrapping Trust Among Microservices a Nightmare

Security aside, how hard would it be to manage 10, 15, or hundreds of independent microservices instead of one monolithic application in a deployment? We have even started seeing microservices deployments with thousands of services talking to each other.

Capital One, one of the leading financial institutions in the United States, announced in July 2019 that its microservices deployment consists of thousands of microservices on several thousands of containers, with thousands of Amazon Elastic Compute Cloud (EC2) instances. Monzo, another financial institution based in the United Kingdom, recently mentioned that it has more than 1,500 services running in its microservices deployment. Jack Kleeman, a backend engineer at Monzo, explains in a blog (http://mng.bz/gyAx) how they built network isolation for 1,500 services to make Monzo more secure. The bottom line is, large-scale microservices deployments with thousands of services have become a reality.

Managing a large-scale microservices deployment with thousands of services would be extremely challenging if you didn’t know how to automate. If the microservices concept had popped up at a time when the concept of containers didn’t exist, few people or organizations would have the guts to adopt microservices. Fortunately, things didn’t happen that way, and that’s why we believe that microservices and containers are a match made in heaven. If you’re new to containers or don’t know what Docker is, think of containers as a way to make software distribution and deployment hassle-free. Microservices and containers (Docker) were born at the right time to complement each other nicely. We talk about containers and Docker in the book, in chapter 10.

Does the deployment complexity of microservices architecture make security more challenging? We’re not going to delve deep into the details here, but consider one simple example. Service-to-service communication happens among multiple microservices. Each of these communication channels must be protected. You have many options (which we discuss in detail in chapters 6 and 7 of the book), but suppose that you use certificates.

Now each microservice must be provisioned with a certificate (and the corresponding private key), which it will use to authenticate itself to another microservice during service-to-service interactions. The recipient microservice must know how to validate the certificate associated with the calling microservice. Therefore, you need a way to bootstrap trust between microservices. Also, you need to be able to revoke certificates (in case the corresponding private key gets compromised) and rotate certificates (change the certificates periodically to minimize any risks in losing the keys unknowingly). These tasks are cumbersome, and unless you find a way to automate them, they’ll be tedious in a microservices deployment.

Requests Spanning Multiple Microservices Are Harder to Trace

Observability is a measure of what you can infer about the internal state of a system based on its external outputs. Logs, metrics, and traces are known as the three pillars of observability.

A log can be any event you record that corresponds to a given service. A log, for example, can be an audit trail that says that the Order Processing microservice accessed the Inventory microservice to update the inventory on April 15th, 2020, at 10:15.12 p.m. on behalf of the user Peter.

Aggregating a set of logs can produce metrics. In a way, metrics reflect the state of the system. In terms of security, the average invalid access requests per hour is a metric, for example. A high number probably indicates that the system is under attack or the first-level defense is weak. You can configure alerts based on metrics. If the number of invalid access attempts for a given microservice goes beyond a preset threshold value, the system can trigger an alert.

Traces are also based on logs but provide a different perspective of the system. Tracing helps you track a request from the point where it enters the system to the point where it leaves the system. This process becomes challenging in a microservices deployment. Unlike in a monolithic application, a request to a microservices deployment may enter the system via one microservice and span multiple microservices before it leaves the system.

Correlating requests among microservices is challenging, and you have to rely on distributed tracing systems like Jaeger and Zipkin. In chapter 5 of the book, we discuss how to use Prometheus and Grafana to monitor all the requests coming to a microservices deployment.

Immutability of Containers Challenges how You Maintain Service Credentials and Access-Control Policies

A server that doesn’t change its state after it spins up is called an immutable server. The most popular deployment pattern for microservices is container based. (We use the terms container and Docker interchangeably in this book, and in this context, both terms have the same meaning.) Each microservice runs in its own container, and as a best practice, the container has to be an immutable server. In other words, after the container has spun up, it shouldn’t change any of the files in its filesystem or maintain any runtime state within the container itself.

The whole purpose of expecting servers to be immutable in a microservices deployment is to make deployment clean and simple. At any point, you can kill a running container and create a new one with the base configuration without worrying about runtime data. If the load on a microservice is getting high, for example, you need more server instances to scale horizontally. Because none of the running server instances maintains any runtime state, you can simply spin up a new container to share the load.

What impact does immutability have on security, and why do immutable servers make microservices security challenging? In microservices security architecture, a microservice itself becomes a security enforcement point. As a result, you need to maintain a list of whitelisted clients (probably other microservices) that can access the given microservice, and you need a set of access-control policies.

These lists aren’t static; both the whitelisted clients and access-control policies get updated. With an immutable server, you can’t maintain such updates in the server’s filesystem. You need a way to get all the updated policies from some sort of policy administration endpoint at server bootup and then update them dynamically in memory, following a push or pull model. In the push model, the policy administration endpoint pushes policy updates to the interested microservices (or security enforcement points). In the pull model, each microservice has to poll the policy administration endpoint periodically for policy updates.

Each microservice also has to maintain its own credentials, such as certificates. For better security, these credentials need to be rotated periodically. It’s fine to keep them with the microservice itself (in the container filesystem), but you should have a way to inject them into the microservice at the time it boots up. With immutable servers, maybe this process can be part of the continuous delivery pipeline, without baking the credentials into the microservice itself.

The Distributed Nature of Microservices Makes Sharing User Context Harder

In a monolithic application, all internal components share the same web session, and anything related to the requesting party (or user) is retrieved from it. In microservices architecture, you don’t enjoy that luxury. Nothing is shared among microservices (or only a very limited set of resources), and the user context has to be passed explicitly from one microservice to another. The challenge is to build trust between two microservices so that the receiving microservice accepts the user context passed from the other one. You need a way to verify that the user context passed among microservices isn’t deliberately modified.

Using a JSON Web Token (JWT) is one popular way to share user context among microservices; we explore this technique in chapter 7 of the book. For now, you can think of a JWT as a JSON message that helps carry a set of user attributes from one microservice to another in a cryptographically safe manner.

Polyglot Architecture Demands More Security Expertise on Each Development Team

In a microservices deployment, services talk to one another over the network. They depend not on each service’s implementation, but on the service interface. This situation permits each microservice to pick its own programming language and the technology stack for implementation. In a multiteam environment, in which each team develops its own set of microservices, each team has the flexibility to pick the most optimal technology stack for its requirements. This architecture, which enables the various components in a system to pick the technology stack that is best for them, is known as a polyglot architecture.

A polyglot architecture makes security challenging. Because different teams use different technology stacks for development, each team has to have its own security experts. These experts should take responsibility for defining security best practices and guidelines, research security tools for each stack for static code analysis and dynamic testing, and integrate those tools into the build process. The responsibilities of a centralized, organization-wide security team are now distributed among different teams. In most cases, organizations use a hybrid approach, with a centralized security team and security-focused engineers on each team who build microservices.


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