Insecure Design in Axum with Mongodb

Insecure design in an Axum service that uses MongoDB often arises when application-level logic does not enforce authorization on a per-request basis and instead relies on broad, role-based access patterns. When routes resolve a shared MongoDB client and perform operations using user-provided identifiers without validating ownership or context, this becomes a BOLA/IDOR pattern that is not detected until runtime. Axum’s type-driven extractor model is powerful, but if design decisions allow unchecked ID propagation into MongoDB queries, attackers can manipulate path or query parameters to reference other users’ documents.

Consider an endpoint defined as GET /users/:user_id/profile where Axum extracts user_id and directly uses it to build a MongoDB filter { "user_id": user_id }. If the API key or token identifying the caller is not cross-checked against the user_id in the query, the design implicitly trusts the client-supplied identifier. This is an insecure design choice because it omits a server-side ownership check, effectively exposing a horizontal privilege escalation surface.

Additionally, embedding sensitive fields in MongoDB documents without explicit projection increases data exposure risk. An Axum handler that retrieves a user document via collection.find_one(filter, None) without specifying a projection may unintentionally return password hashes, session tokens, or internal metadata to the caller. If the response is serialized with a generic serializer (e.g., JSON), sensitive fields can be transmitted inadvertently. The design should enforce field-level inclusion and consistently apply the principle of least data.

Rate limiting and input validation design also interact with MongoDB usage. If Axum routes do not enforce request-rate controls at the route level and MongoDB operations do not validate bounds on array sizes or string lengths, the service becomes susceptible to injection-style data exhaustion or NoSQL injection. For example, failing to validate that a filter_key parameter is alphanumeric before inserting it into a MongoDB query structure can allow operators or regex patterns that change query semantics. Designing endpoints without pre-validated schemas and server-side rate policies shifts burden to the database and increases the likelihood of unintended data access paths.

Finally, logging designs that include full MongoDB responses or stack traces in Axum middleware can amplify information exposure. If handler errors are serialized with full context including database internals, an attacker can learn about collection names, index structures, or timing characteristics that aid further exploitation. A secure design treats MongoDB responses as sensitive, strips or redacts them before logging, and uses typed, bounded responses that reveal only what is necessary for the client.

Remediation focuses on explicit ownership checks, strict input validation, and least-privilege data handling. In Axum, model your extractors so that the authenticated subject (e.g., sub claim or API key) is available before any MongoDB operation. Use a typed wrapper around the MongoDB client that enforces tenant or user scoping for every query, ensuring that filters always include the authenticated subject alongside the requested identifier.

Example: Define a guarded handler that resolves the caller’s identity from an authorization extractor and combines it with the route identifier before building the filter.

use axum::{routing::get, Router, extract::State};
use mongodb::{Client, Collection};
use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize)]
struct ProfileParams {
    user_id: String,
}

#[derive(Debug, Serialize)]
struct SafeProfile {
    user_id: String,
    display_name: String,
}

async fn get_profile(
    State(client): State,
    params: ProfileParams,
    claims: AuthClaims,
) -> Result {
    let users: Collection<bson::Document> = client.database("appdb").collection("users");
    // Enforce ownership: combine route param with authenticated subject
    let filter = bson::doc! {
        "user_id": &params.user_id,
        "sub": &claims.sub,
    };
    let doc = users.find_one(filter, None).await.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
    let doc = doc.ok_or(StatusCode::NOT_FOUND)?;
    let safe = SafeProfile {
        user_id: doc.get_str("user_id").map_err(|_| StatusCode::FORBIDDEN)?.to_string(),
        display_name: doc.get_str("display_name").map_err(|_| StatusCode::FORBIDDEN)?.to_string(),
    };
    Ok(safe)
}

pub fn app_routes() -> Router<Client> {
    Router::new()
        .route("/profile", get(get_profile))
        .with_state(MongoClient::new("mongodb://localhost:27017"))
}

This pattern ensures that the user identifier cannot be leveraged to read another user’s data because the backend always adds the subject constraint. It also avoids returning sensitive fields by projecting only the needed keys if using a cursor-based approach, or by mapping to a safe DTO as shown.

For input validation, apply strict checks on identifiers before they reach MongoDB. Use a validator that enforces format constraints (e.g., ObjectId format or UUID) and rejects operators or special characters that could alter query semantics.

use validator::Validate;

#[derive(Debug, Validate)]
struct ValidatedProfileParams {
    #[validate(length(min = 12, max = 12), regex = "^[a-f0-9]{24}$")]
    user_id: String,
}

async fn validated_profile(
    State(client): State,
    params: ProfileParams,
    claims: AuthClaims,
) -> Result<SafeProfile, StatusCode> {
    let validated = ValidatedProfileParams::validate(params).map_err(|_| StatusCode::BAD_REQUEST)?;
    let users: Collection<bson::Document> = client.database("appdb").collection("users");
    let filter = bson::doc! {
        "user_id": &validated.user_id,
        "sub": &claims.sub,
    };
    // ... same safe mapping as before
}

To mitigate data exposure, explicitly define projection to return only required fields and avoid returning internal metadata. If using MongoDB’s Rust driver, prefer strongly typed documents or manual decoding instead of raw BSON when possible. Combine this with Axum’s rejection handling to ensure malformed or over-privileged requests never reach the database in an unchecked state. Finally, design logging to exclude raw MongoDB responses; log only request identifiers and outcome codes to reduce the information disclosed during incidents.