Injection Flaws in Axum with Mongodb

Injection flaws occur when untrusted data is sent to an interpreter as part of a command or query. In an Axum service that uses MongoDB as the backend, the risk arises when user-supplied input is concatenated into MongoDB queries instead of being handled through typed, schema-driven operations. Axum is a Rust web framework that is type-safe and encourages structured handlers, but developers can still introduce injection by manually building BSON documents or using string-based pipelines.

For example, constructing a filter document via string concatenation or loosely typed JSON can lead to query injection, where an attacker manipulates the effective query logic. Consider an endpoint that searches user profiles by a string parameter. If the handler builds a BSON document by interpolating the parameter directly, an input like {"$ne": ""} can alter the semantics of the query. In MongoDB, operators such as $ne, $in, and $regex can be abused to bypass intended filters or extract unintended documents.

Another common pattern is using string-based map-reduce or aggregation pipelines built from user input. If stage definitions or field names are not strictly validated, an attacker can inject pipeline stages or object identifiers that change behavior or data scope. Since MongoDB supports expressive query syntax, unsanitized inputs can lead to conditions that return more data than intended, enabling data exposure or unauthorized access across tenant boundaries (BOLA/IDOR-like outcomes).

In Axum, handlers often deserialize request parameters into structs using Serde. Injection risk increases when developers bypass this structured deserialization and instead work with raw bson::Document or serde_json::Value to build queries. This can expose the application to injection through unexpected operator keys or malformed paths. Additionally, if indexes are dynamically built from user input without strict allow-listing, the query planner may behave differently, potentially exposing sensitive data or degrading performance in ways that aid reconnaissance.

Real-world attack patterns include attempts to exfiltrate data via crafted query operators or to manipulate sort orders to bypass pagination controls. While Axum does not inherently introduce these risks, the combination of dynamic query building and MongoDB’s flexible query language creates an injection surface that must be managed through strict input validation and schema-based construction of queries.

Remediation focuses on avoiding string-based query construction and leveraging MongoDB’s typed query builders and schema validation. In Rust with the official MongoDB driver, prefer using the doc! macro and strongly typed filter structs. Never directly interpolate user input into BSON paths or keys. Instead, validate and map inputs to known fields before constructing queries.

Example of a vulnerable pattern to avoid:

// DO NOT DO THIS — vulnerable to injection
let filter = doc! {
    "username": username, // username from user input
};
// If username is controlled by an attacker and contains operators, behavior changes.

Safe alternative using typed structures and allow-listing:

use mongodb::bson::{doc, Document};
use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize, Serialize)]
struct User {
    username: String,
    email: String,
}

async fn find_user_by_username(
    collection: &mongodb::Collection,
    raw_username: &str,
) -> Result, mongodb::error::Error> {
    // Validate input: allow only alphanumeric and limited special characters
    if !raw_username.chars().all(|c| c.is_alphanumeric() || c == '_' || c == '-') {
        return Err(mongodb::error::Error::from(std::io::Error::new(
            std::io::ErrorKind::InvalidInput,
            "Invalid username format"[..],
        )));
    }
    let filter = doc! {
        "username": raw_username,
    };
    collection.find_one(filter, None).await
}

For dynamic queries where field names must be selected from a controlled set, use an enum to map allowed values:

#[derive(Debug)]
enum UserField {
    Username,
    Email,
}

impl ToString for UserField {
    fn to_string(&self) -> String {
        match self {
            UserField::Username => "username".to_string(),
            UserField::Email => "email".to_string(),
        }
    }
}

async fn build_safe_projection(
    field: UserField,
    value: &str,
) -> Document {
    doc! {
        field.to_string(): value,
        "email": 1,
    }
}

When using aggregation pipelines, construct stages programmatically rather than concatenating JSON strings. Use the MongoDB Rust driver’s builder APIs to add match, project, and limit stages safely:

use mongodb::bson::doc;
use mongodb::options::AggregateOptions;

async fn safe_aggregate(
    collection: &mongodb::Collection,
    user_id: &str,
) -> Result, mongodb::error::Error> {
    let pipeline = vec![
        doc! {
            "$match": {
                "_id": user_id
            }
        },
        doc! {
            "$project": {
                "username": 1,
                "email": 1,
                "_id": 0,
            }
        },
    ];
    let mut cursor = collection.aggregate(pipeline, AggregateOptions::default()).await?;
    let mut results = Vec::new();
    while let Some(doc) = cursor.next().await {
        results.push(doc?);
    }
    Ok(results)
}

Additionally, enable schema validation on the collection where possible to reject documents that do not conform to expected shapes. This provides a secondary layer of protection against malformed or malicious input that might otherwise bypass application-level checks.