AWS Smithy IDL: A Technical Overview with a Focus on Security

In the world of web service APIs, the interface definition language (IDL) you choose can significantly impact both development and security. 

Smithy, an open-source IDL created by AWS, offers a compelling option, especially for those working within service-oriented architectures. This blog post provides a technical overview of Smithy, focusing on how it integrates with service-oriented architecture and the security benefits it provides.

What is Smithy and How It Fits into Service-Oriented Architecture

Smithy is a protocol-agnostic IDL and toolset for defining web service APIs. In a service-oriented or microservices architecture, you typically define clear interface contracts for each service—Smithy was designed to model those service contracts. A Smithy model describes a service as a collection of resources, operations, and data shapes (structures, enums, etc.). From this single model, Smithy can generate everything from client SDKs in multiple languages to server skeletons, API documentation, and other artifacts.

In practice, Smithy allows teams to separate API definition from implementation. For example, an organization can write a Smithy model for a service (describing its operations and data types) and then generate client libraries or stubs in Java, Python, Rust, or other languages. This enables large-scale collaboration on APIs and ensures all clients are consistent with the service’s contract. In fact, AWS itself uses Smithy to model thousands of services and to generate the official AWS SDKs in dozens of programming languages. By using Smithy as the source of truth for API definitions, teams can avoid the error-prone work of hand-writing client code for each language and keep their interface changes synchronized across the system.

Another way Smithy fits into service-oriented design is through its support for resource-oriented modeling. Smithy’s IDL has first-class concepts for resources and standard CRUD-style operations. Instead of just RPC calls, you model resources (entities) with identifiers and attach operations to them (e.g., a City resource with GetCity or ListCities operations). This aligns well with RESTful API design and helps architects think in terms of service resources rather than low-level messages. Under the hood, Smithy is protocol-agnostic – meaning the model doesn’t hard-code whether it’s REST/HTTP, gRPC, or any specific protocol. You can later bind the model to concrete protocols (like HTTP+JSON) via traits, but the core model remains abstract. This abstraction is powerful in a microservice environment: the same Smithy model could be realized as a RESTful JSON API, or potentially as an MQTT or gRPC service, without changing the model definition.

In summary, Smithy provides an intuitive, declarative way to design service APIs that fits naturally into service-oriented architectures. It codifies best practices learned from AWS’s experience building APIs and acts as a bridge between design and implementation. By using Smithy, you ensure that your service’s contract (methods, inputs/outputs, errors) is clearly defined and can be automatically enforced and propagated to all parts of your system (clients, servers, docs, tests), which is especially valuable as your architecture grows.

What Makes Smithy Different from Other IDLs

Smithy distinguishes itself from other IDLs (like gRPC's Protocol Buffers or OpenAPI) with its protocol-agnostic design, resource-based modeling, extensible trait system, built-in model validation, strong code generation, and compatibility. It allows for describing a broad range of services beyond HTTP/REST, models services in an abstract way, enables custom metadata through traits, enforces API standards via validators, generates high-quality code for multiple languages, and integrates well with AWS services. Smithy is a flexible, extensible, and tool-friendly language designed to scale, ensure consistency, and target multiple protocols and languages, making it a robust choice for API modeling.

Security Best Practices for Using and Extending Smithy

Now let’s focus on using Smithy from a security perspective. When modeling APIs and integrating Smithy into your toolchain, it’s important to leverage Smithy’s features to enforce security and to be mindful of how custom extensions might impact the security of your system. Below are several best practices and tips:

1. Secure API Modeling (Authentication and Authorization)

Best practice: Use Smithy’s auth traits to declare the authentication requirements of your API upfront. This makes the contract clear and allows generated code to incorporate auth (e.g., clients can automatically sign requests, servers can enforce auth checks). Even if Smithy doesn’t enforce the auth at the IDL level, documenting it in the model is valuable. If none of the built-in auth traits fit your needs (say you have a custom token scheme), you can create a custom auth trait using Smithy’s meta-trait @authDefinition to define your own authentication scheme. Ensuring every operation has an auth strategy (even if it’s “none” for public endpoints) is important for not leaving security to chance.

In addition to authentication, use Smithy’s resource-based design to your advantage for authorization. By modeling resources and sub-resources explicitly, you can align your API with authorization scopes. For example, if you have a Account resource and a AdminReport sub-resource, it’s clear in the model that AdminReport operations might require elevated permissions. You might even create a custom trait like @requiresRole("Admin") on certain operations as documentation. While such custom traits won’t automatically enforce anything, they serve as a machine-readable contract that you can utilize in code generation or documentation to ensure only authorized clients call those operations.

2. Data Sensitivity and Privacy

Smithy allows you to mark data structures with metadata that indicate sensitive information. Always review your shapes and mark any sensitive fields with the @sensitive trait. The @sensitive trait is a simple annotation that tells anyone using the model (and code generators) that the data “is sensitive and MUST be handled with care.” According to Smithy’s documentation, data marked sensitive must not be exposed in logs or error messages. For example, if you have a structure User with a field password, you should annotate password: String @sensitive. Many Smithy code generators (including AWS’s) will honor this by redacting or avoiding printing such fields in logs. This is a security best practice to prevent accidental leakage of PII or credentials.

In addition to @sensitive, consider using the @internal trait for shapes that should not be exposed to external consumers. Smithy’s @internal is useful if you maintain a single model that includes both public API and internal API (for example, extra fields used only internally). Marking a shape or member as @internal signals that it’s not meant for external clients, and tooling can strip those out when generating external-facing artifacts. By properly segmenting internal vs external parts of your model, you reduce the risk of exposing something unintentionally. If you plan to publish your Smithy models or use them to generate public SDKs, you could even set up a Smithy projection to drop internal shapes (the Smithy build system allows filtering the model by traits).

Validation of input data is another aspect of security. Smithy provides constraint traits (like @length, @pattern, @range, etc.) which you can apply to shape definitions to constrain the values that are allowed. For instance, you might enforce @length(min:1, max:256) on a username string, or @pattern on an email field. These constraints will be carried into generated server-side code as runtime checks (or at least documented for enforcement). Best practice is to encode your input validation requirements in the Smithy model itself. By doing so, any client or server generated from the model knows about these rules. Smithy specifies that constraint traits are for validation only and should be enforced when deserializing inputs (not on outputs). This means your service should reject bad input (e.g., too long a string or an out-of-range number) early, before processing, which can prevent certain attacks (like buffer overflows or injection attacks that rely on unexpectedly large or malformed data). Using Smithy’s constraint traits centralizes these rules in your model. And if the built-in constraints aren’t enough, you can define custom constraint traits and write validators (for example, a @mustBeEmail trait with a custom Java validator to check format). This ensures consistency across implementations and avoids duplicate validation logic scattered in code.

3. Custom Traits and Extending Smithy Securely

When you extend Smithy with your own traits or integrate Smithy into custom tooling, treat those extensions with the same scrutiny as you would any security-sensitive code. Define clear semantics for custom traits and use Smithy’s mechanisms to validate them. For example, if you introduce a trait @encrypted to mark fields that should be encrypted at rest, you might also implement a Smithy validator plugin that emits an error if any @encrypted field is of a type that can’t be encrypted, or if it’s missing in certain contexts. Smithy’s validation system allows writing custom validators in Java and hooking them into the build. This is a powerful way to enforce security rules. As an illustration, you could write a validator that checks that every operation has at least one auth scheme trait applied (to catch any unprotected endpoint in the model before it goes live).

Be cautious with how custom trait data is used in code generation. Since traits can carry structured data, there’s a risk (albeit small) of things like code injection if a code generator naively inserts trait values into output code. Always sanitize or properly handle trait values in templates. In practice, if you’re using Smithy’s official codegen libraries, they will treat trait values as data, but if you write your own template, e.g. generating a piece of code based on a trait string, ensure you escape it appropriately for the target language. For example, a trait might include a snippet of text for documentation – if you inject that into a Java string without escaping quotes, it could break the code. This is more of a general codegen caution, but it’s part of secure toolchain usage: validate and sanitize model-derived data when generating code or configuration.

Another best practice for custom traits is to use namespaces and tagging to avoid confusion. Give your traits a unique namespace (usually your organization or project) so they don’t collide with others. For instance, com.mycompany.security#encrypted is better than just encrypted. You can also tag traits with metadata. Smithy has a tags trait that can group traits or shapes, which could be used to tag all security-related traits. This might help tooling (e.g., you could have a docs generator that highlights all traits tagged "security").

4. Validation and CI/CD Integration

Leverage Smithy’s validation not just during development but in your continuous integration pipeline. Always run smithy validate (or the Smithy build) as part of your build process to catch model errors or violations of your organization’s standards early. The Smithy CLI makes this easy to do with a single command. You can also use Smithy Diff (smithy diff) in CI to detect any breaking changes in your API model before they get merged. Smithy Diff will compare a proposed model (e.g., in a pull request) against a baseline (perhaps the last released model) and report if any changes are backward-incompatible or unexpected. This is extremely useful from a security perspective because it can, for example, flag if someone accidentally removed an auth requirement or widened the type of a field (which might expose more data) without intending to. Treat your Smithy model changes like code changes: review them, diff them, and test them.

If you have an automated deployment (CI/CD) for your services, include steps to generate artifacts from Smithy and run security scans. For instance, if Smithy generates an OpenAPI spec for documentation, you might run that through an API security scanner. Or if Smithy generates code, ensure that code goes through your normal static analysis and security testing. The good news is that because Smithy encourages generation from a single source, you reduce the chance of inconsistencies (one of the security benefits is that you don’t have multiple divergent API descriptions). But you should still audit the generated outputs, especially early in adoption, to ensure the generators are producing secure code/config (e.g., check that clients enforce TLS, or that server stubs don’t disable authentication checks).

Keep the Smithy tools up to date. Like any tool, the Smithy CLI and libraries might receive updates that fix bugs or improve security. Upgrading promptly ensures you benefit from the latest validations and fixes. The Smithy project has a CHANGELOG and releases on GitHub you can monitor. Since Smithy is relatively young (IDL 2.0 was released in 2022), expect ongoing improvements – some of which could be security-relevant (for example, improvements in codegen for authentication or stricter default validations).

Lastly, if you use Smithy to generate service stubs or clients, don’t assume it covers all security aspects. You still need to implement the actual authentication enforcement in your service logic and handle authorization checks. Smithy will help you by making sure the structure for security is in place (auth data is passed, errors like 401/403 can be modeled, etc.), but it’s not a magic wand – the runtime behavior is up to your code. Use Smithy’s modeling to make security requirements explicit and use its tooling to prevent mistakes, but follow through in your implementation and testing. For example, if a certain field should be encrypted, marking it @sensitive in Smithy is step one; step two is ensuring your service actually encrypts it wherever it’s stored or transmitted.

5. Secure Toolchain Integration

Integrating Smithy into your development toolchain should be done with an eye on security. The Smithy CLI (and Gradle plugin) can be run on developer machines and CI servers. Make sure that the environment running these tools is secure, because the Smithy build will process your model files (and any included models). Model files are code – treat them as such, with proper version control and reviews. If you pull in any third-party Smithy models or traits (for example, maybe you include some open-source Smithy model for a standard API), verify the source and integrity of those just as you would an open-source library. Malicious or incorrect model definitions could introduce vulnerabilities in generated code (imagine a trait that causes a generator to produce insecure code if not handled correctly).

If you write custom Smithy build plugins (Java code that gets executed during Smithy builds for validations or projections), be careful with dependency management. Those plugins run as part of the build process, so ensure they don’t introduce unsafe behavior or expose you to supply chain risks. It’s advisable to keep the set of Smithy plugins small and well-reviewed.

On a brighter note, using Smithy can improve your overall security stance by making reviews easier. Because Smithy models are fairly concise and declarative, a security reviewer or architect can read the Smithy IDL to understand the API surface quickly. This is often easier than reading code. Encourage your teams to perform threat modeling and security reviews at the Smithy model level. For example, look at each operation in the model: does it expose data it shouldn’t? Are the error conditions modeled correctly (no information leakage in error messages)? Are all request inputs validated via constraints? By catching issues in the model, you can fix them before code is generated or written. This shifts security left in the development lifecycle.

In conclusion, Smithy’s rich type system, trait metadata, and validation framework give you the tools to design secure APIs from the start. Use them to enforce authentication, mark sensitive data, validate inputs, and keep internal details hidden. Integrate Smithy validation and diffing into your pipeline to prevent accidental security regressions. And when extending Smithy, do so in a principled way – leveraging its features to maintain or enhance security rather than bypass it. With these practices, Smithy can significantly aid in building robust, secure services.

Conclusion

Smithy is an AWS-developed Interface Definition Language (IDL) for API modeling, offering protocol-agnostic and trait-driven service definition. It enables auto-generation of clients/servers, API standard enforcement, and safe API evolution. Security-wise, Smithy encourages proactive definition of security requirements at the model level, emphasizing features like authentication traits and data sensitivity marking. Using Smithy fosters a "design-first, security-conscious API development" culture, facilitating robust implementations and easier maintenance, and promoting "design for security" in complex systems.