Api Key Exposure in Axum with Mongodb

When building a REST or GraphQL API with Axum and using a Mongodb backend, developers often store database credentials, third-party service tokens, or internal API keys in environment variables or configuration files. If Axum request handling inadvertently includes these keys in HTTP responses, logs, or error messages, the application can expose sensitive credentials. This exposure commonly occurs when Axum routes serialize structs or debug information that contain fields mapped to Mongodb connection strings or key names.

For example, an Axum handler that returns a full database record may include a mongodb_uri field if the data model is not carefully designed. Even without direct access to the database, an attacker who can trigger verbose error pages or improperly structured JSON responses may obtain these keys. The risk is compounded when Axum applications run behind misconfigured reverse proxies or when development routes are left enabled in production.

middleBrick scans Axum-based APIs to detect whether API keys or sensitive configuration values appear in endpoint responses, headers, or error payloads. One relevant check is Property Authorization, which validates that sensitive properties are not returned to unauthenticated callers. Another is Data Exposure, which inspects responses for patterns resembling API keys, secrets, or credentials. When combined with the OpenAPI/Swagger analysis, middleBrick cross-references your spec definitions with runtime findings to highlight places where key-like values may leak through your Axum routes.

LLM/AI Security checks are particularly valuable for this scenario because they test whether prompts or system instructions can cause the application to reveal internal configuration, such as connection strings or administrative keys. Axum services that expose endpoints used by AI tooling or that embed configuration in prompts are at risk of system prompt leakage or data exfiltration via crafted inputs. middleBrick’s active prompt injection testing probes these paths to ensure keys are not derivable from manipulated requests.

Because Axum is a Rust web framework, developers may assume type safety prevents leaks; however, improper use of serialization libraries or logging macros can still surface sensitive fields. middleBrick helps identify these issues by running 12 security checks in parallel, including Authentication, BOLA/IDOR, and Unsafe Consumption, ensuring that Axum endpoints interacting with Mongodb do not unintentionally broadcast credentials.

To prevent API key exposure when Axum communicates with Mongodb, structure your data models so that sensitive fields are omitted from serialization responses. Use dedicated response structs that exclude database connection details, internal IDs, or administrative keys. Avoid returning raw Mongodb documents directly from handlers; instead, map them to safe representations.

Below are concrete Axum examples with Mongodb integration that demonstrate secure handling of keys.

// Cargo.toml dependencies
// axum = "0.6"
// mongodb = "2.8"
// serde = { version = "1.0", features = ["derive"] }
// tokio = { version = "1", features = ["full"] }

use axum::{routing::get, Router};
use mongodb::{Client, options::ClientOptions};
use serde::{Deserialize, Serialize};

// Domain model (internal)
#[derive(Debug, Deserialize)]
struct InternalUser {
    id: mongodb::bson::oid::ObjectId,
    username: String,
    mongodb_uri: String, // sensitive, should not be exposed
}

// Safe response model (public)
#[derive(Debug, Serialize)]
struct PublicUser {
    id: String,
    username: String,
}

impl From for PublicUser {
    fn from(internal: InternalUser) -> Self {
        PublicUser {
            id: internal.id.to_string(),
            username: internal.username,
        }
    }
}

#[tokio::main]
async fn main() {
    // Connect to Mongodb without exposing credentials in responses
    let client_options = ClientOptions::parse("mongodb+srv://user:[email protected]/db")
        .await
        .expect("valid connection string");
    let client = Client::with_options(client_options).expect("valid client options");
    let db = client.database("app_db");
    let users_collection = db.collection::("users");

    let app = Router::new()
        .route("/users/:id", get(move |axum::extract::Path(id): axum::extract::Path| async move {
            let oid = mongodb::bson::oid::ObjectId::parse_str(&id).unwrap();
            let result = users_collection.find_one(mongodb::bson::doc! { "_id": oid }, None).await;
            match result {
                Ok(Some(doc)) => {
                    // Explicitly convert to safe response
                    let public_user = PublicUser::from(doc);
                    axum::Json(public_user)
                }
                Ok(None) => axum::response::IntoResponse::into_response(axum::http::StatusCode::NOT_FOUND),
                Err(e) => {
                    // Log errors without exposing keys
                    tracing::error!(error = %e, "db error");
                    axum::response::IntoResponse::into_response(axum::http::StatusCode::INTERNAL_SERVER_ERROR)
                }
            }
        }));

    axum::Server::bind(&"0.0.0.0:3000".parse().unwrap())
        .serve(app.into_make_service())
        .await
        .unwrap();
}

Key practices include:

• Never include raw Mongodb connection strings or administrative keys in structs that are serialized for HTTP responses.
• Use the #[serde(skip_serializing)] attribute on sensitive fields if you must retain them in shared structs.
• Validate and sanitize all inputs that influence Mongodb queries to prevent injection or data leakage through error messages.
• Configure Axum logging to avoid printing full request or response bodies that may contain key-like values.

middleBrick’s per-category breakdowns, such as Property Authorization and Data Exposure, help verify that these mitigations are effective by comparing your OpenAPI spec against runtime behavior. The CLI tool (middlebrick scan <url>) can be integrated into development workflows to continuously check for regressions, while the Pro plan’s continuous monitoring can schedule regular scans of your Axum endpoints to catch new exposure risks early.