Identification Failures in Axum with Mongodb

Identification failures occur when an API incorrectly identifies or infers a user or resource, allowing one actor to assume the identity of another. In an Axum application using MongoDB as the data store, this typically manifests as Insecure Direct Object References (IDOR) or Broken Object Level Authorization (BOLA). Axum routes map incoming requests to handlers that often extract an identifier (e.g., user_id) from the path or query parameters and use it to construct a MongoDB query. If the identifier is user-supplied and not properly validated against the authenticated subject’s permissions, an attacker can tamper with the identifier to access or modify other users’ data.

Consider an endpoint /users/{user_id}/profile. An Axum handler might parse user_id from the path, then build a MongoDB filter like { _id: user_id }. If the handler relies solely on the path parameter without confirming that the authenticated principal owns that _id, an attacker can change user_id to another valid ObjectId and retrieve or alter another user’s profile. This is an identification failure because the authorization boundary is based only on an external, mutable identifier rather than on the authenticated identity.

MongoDB’s flexible schema and rich query language can inadvertently enable identification failures when indexes and queries are not aligned with access control. For example, if a query uses a string identifier while the collection stores ObjectId values, implicit type conversions may match unintended documents. Additionally, missing compound indexes that enforce tenant or user context can cause queries to return more than one document, leading to data leakage across users. In Axum, if the handler does not enforce a strict one-to-one mapping between the authenticated subject and the query predicate, the API surface broadens for attackers to probe other valid identifiers.

These issues are amplified when APIs expose natural keys or sequential identifiers. An attacker can iterate over plausible values (IDsOR) or exploit weak rate limiting to map valid resources. Because MongoDB returns distinct error types for missing vs. unauthorized documents, timing differences may leak existence. MiddleBrick’s checks for BOLA/IDOR and Property Authorization highlight these risks by correlating OpenAPI path parameters with runtime access patterns, ensuring that identifiers are bound to the authenticated subject rather than being interpreted as generic references.

In the Axum + MongoDB stack, identification failures are not solely a routing issue; they emerge from the interaction between route extraction, data access patterns, and authorization logic. Without explicit checks that the authenticated identity matches the queried identifier, even well-structured MongoDB queries can expose data across users. Applying middleware that binds the subject to every query and validating identifiers against a consistent access model reduces the attack surface and closes identification gaps.

Remediation centers on ensuring that every MongoDB query includes the authenticated subject as a mandatory filter component, and that identifiers are validated and normalized before use. In Axum, implement a layer that enforces identity binding and strict type handling so that users can only access their own documents.

First, derive the authenticated subject from your auth scheme (e.g., JWT) and pass it to handlers via request extensions or state. Then construct queries that combine the subject with any user-supplied identifier. For example, if each document has a user_id field of type ObjectId, bind the subject’s ObjectId to the query rather than relying on the path parameter alone:

use axum::{routing::get, Router, extract::State};
use mongodb::{Client, Collection, bson::doc, bson::oid::ObjectId};
use serde::Deserialize;

struct AppState {
    users: Collection,
    subject_id: ObjectId, // derived from authenticated session
}

#[derive(Deserialize)]
struct ProfilePath {
    user_id: String, // user-supplied, potentially unsafe
}

async fn get_profile(
    State(state): State,
    path: axum::extract::Path,
) -> Result {
    // Validate and normalize the user_id; reject non-ObjectId values early
    let target_id = match ObjectId::parse_str(&path.user_id) {
        Ok(id) => id,
        Err(_) => return Err((axum::http::StatusCode::BAD_REQUEST, "Invalid user_id".into())),
    };

    // Critical: ensure the queried document belongs to the authenticated subject
    let filter = doc! {
        "_id": target_id,
        "user_id": state.subject_id,
    };
    let result = state.users.find_one(filter, None).await.map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    match result {
        Some(doc) => Ok(doc.to_string()),
        None => Err((axum::http::StatusCode::FORBIDDEN, "Access denied".into())),
    }
}

This pattern ensures that even if an attacker manipulates user_id, the query will return no match unless the identifier maps to a document that also carries the authenticated subject’s user_id. The combination of _id and user_id in the filter enforces a dual-key check that aligns with the principle of least privilege.

Second, use compound indexes to enforce uniqueness and improve performance. For the above filter, create an index on { user_id: 1, _id: 1 } so MongoDB can efficiently verify both conditions:

use mongodb::IndexModel;
use mongodb::bson::{doc, Document};

let index = IndexModel::builder()
    .keys(doc! { "user_id": 1, "_id": 1 })
    .unique(true)
    .build();
state.users.index_manager().create_one(index, None).await.map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

Third, normalize identifiers to ObjectId server-side and avoid echoing raw user input into responses. MiddleBike’s findings for Identification Failures include mappings to OWASP API Top 10 A01 and A07, so ensure your implementation reflects these mitigations. For continuous assurance, you can use the middleBrick CLI to scan from terminal with middlebrick scan <url> or integrate the GitHub Action to add API security checks to your CI/CD pipeline, failing builds if risk scores drop below your chosen threshold.