Insecure Deserialization in Axum with Mongodb

Insecure deserialization occurs when an application processes untrusted data into objects without strict validation or type constraints. In an Axum service that persists or queries data in Mongodb, deserialization risks arise at two integration points: (1) when Axum extracts and decodes request payloads into Rust structures, and (2) when application code manually decodes or reconstructs BSON from Mongodb without schema enforcement.

Consider an Axum handler that accepts JSON intended to be stored in Mongodb. If the handler uses a permissive deserialization approach or reconstructs BSON manually, attacker-controlled input can lead to unexpected object graphs. For example, an attacker might embed nested arrays or special BSON types (e.g., undefined, dbpointer, javascript) that, when later interpreted by application logic or by a less strict downstream consumer, trigger unintended behavior. Axum’s extractors (e.g., Json<T>) rely on Serde; if the expected type is too generic (such as serde_json::Value or a loosely defined struct), malicious payloads can traverse into fields that the application later uses in Mongodb queries. This can facilitate injection-like effects, including query manipulation or prototype pollution in server-side JavaScript logic if Mongodb evaluates expressions.

Another common pattern is using Mongodb’s BSON documents directly as Axum request bodies or responses. If application code deserializes BSON with a custom or unsafe decoder that does not enforce strict type boundaries, remote attackers may supply crafted BSON that deserializes into unexpected Rust types, potentially leading to logic bypass or information disclosure. For instance, an attacker might exploit differences between BSON and JSON representations to inject values that change authorization checks performed in Rust before issuing Mongodb operations. Because Axum does not enforce schema validation by default, developers must explicitly validate inputs and ensure that Mongodb queries use strongly typed structures rather than raw document reconstruction.

Insecure deserialization in this combination is amplified when developers rely on implicit conversions, such as constructing BSON documents by string concatenation or by loosely typed builders without verifying field types. Attack patterns can include tampering with identifiers used in lookups, escalating privileges via manipulated role fields, or exfiltrating data through carefully shaped inputs that exploit lenient deserialization in downstream services. Continuous scanning with a tool like middleBrick is valuable here: its API checks can detect weak input validation, missing authorization on sensitive endpoints, and inconsistencies between OpenAPI contracts and runtime behavior, helping teams identify insecure deserialization paths before attackers do.

To mitigate insecure deserialization in Axum with Mongodb, enforce strict schema validation at the boundary and avoid manual BSON reconstruction. Use strongly typed structs with Serde and validate every field, including enums and numeric ranges. Prefer typed builders for BSON and ensure Mongodb queries are constructed from validated, typed objects rather than raw strings or loosely typed maps.

Example of a vulnerable pattern to avoid:

// Avoid: permissive deserialization and raw BSON construction
use axum::extract::Json;
use serde_json::Value;
async fn create_user(Json(payload): Json<Value>) {
    let doc = bson::to_document(&payload).unwrap(); // unsafe
    collection.insert_one(doc, None).await.unwrap();
}

Remediation with strict typing and validation:

use axum::extract::Json;
use mongodb::{bson::{doc, oid::ObjectId}, Collection};
use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize, Serialize)]
struct CreateUser {
    email: String,
    role: UserRole,
}

#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
enum UserRole {
    Admin,
    User,
}

async fn create_user(
    Json(payload): Json<CreateUser>,
    collection: Extension<Collection>,
) -> Result<impl IntoResponse, (StatusCode, String)> {
    let doc = doc! {
        "email": payload.email,
        "role": match payload.role {
            UserRole::Admin => "admin",
            UserRole::User => "user",
        },
    };
    collection.insert_one(doc, None).await.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(StatusCode::CREATED)
}

This approach ensures that only expected fields and types are accepted. For queries, use typed filters and avoid building BSON by string interpolation:

use mongodb::bson::doc;

let filter = doc! { "email": user_input.email };
let result = collection.find_one(filter, None).await;

Additionally, apply schema validation at the database level where possible (JSON Schema validation rules in Mongodb) and enable server-side checks to reject malformed BSON. Combine these practices with runtime input validation libraries and continuous security scans, such as those provided by middleBrick, to detect insecure deserialization patterns and ensure alignment with frameworks like OWASP API Top 10 and compliance regimes including SOC2 and GDPR.