Nosql Injection in Axum

Nosql injection in Axum applications typically occurs when user input is incorporated directly into MongoDB queries without proper sanitization. In Axum, this often happens in route handlers that accept query parameters, path parameters, or JSON bodies and use them to construct MongoDB queries.

The most common attack pattern involves exploiting MongoDB's query operators. An attacker can inject operators like $where, $regex, or $ne to manipulate query logic. For example, a login endpoint might compare a username and password directly to database fields:

async fn login(db: Data<MongoDb>, user: Json<LoginRequest>) -> Result<Json<User>> {  // Vulnerable: direct query construction  let query = doc! {    "username": user.username,    "password": user.password  };  let result = db.collection("users").find_one(query, None).await?;  // ...}

An attacker could submit {"username": "admin", "password": {"$ne": null}} to bypass authentication entirely. The $ne operator means "not equal," so the query returns any user where the password doesn't equal null—likely all users.

Another common pattern in Axum involves ObjectId injection. When Axum extracts path parameters as strings for MongoDB queries, attackers can manipulate these values. Consider:

async fn get_user(db: Data<MongoDb>, Path(user_id): Path<String>) -> Result<Json<User>> {  // Vulnerable: no ObjectId validation  let query = doc! {    "_id": user_id  };  let result = db.collection("users").find_one(query, None).await?;  // ...}

An attacker could submit a crafted user_id containing MongoDB operators, potentially accessing other users' data or causing query errors.

Property injection is particularly dangerous in Axum applications. When user input is used to construct query field names or nested queries, attackers can traverse collections or access unintended data:

async fn search(db: Data<MongoDb>, query: Json<SearchRequest>) -> Result<Json<Vec<Product>>> {  // Vulnerable: dynamic field access  let mongo_query = doc! {    query.field: {      "$regex": query.term    }  };  let results = db.collection("products").find(mongo_query, None).await?;  // ...}

If query.field isn't validated, an attacker could specify fields like "$where" or traverse relationships to access sensitive data.

Detecting NoSQL injection in Axum requires examining both the code structure and runtime behavior. In Axum applications, the framework's extractor system (Path, Query, Json) makes it straightforward to identify where user input enters the system.

Static analysis should focus on route handlers that accept user input and immediately use it in MongoDB queries. Look for patterns where Path<String>, Query<String>, or Json<YourStruct> values are directly inserted into doc! macros without validation:

async fn vulnerable_route(  db: Data<MongoDb>,  Path(user_input): Path<String>) -> Result<Json<User>> {  // High risk: direct insertion into query  let query = doc! { "username": user_input };  // ...}

middleBrick's black-box scanning approach is particularly effective for Axum APIs because it tests the actual runtime behavior. The scanner sends crafted payloads to your endpoints and analyzes responses for NoSQL injection indicators like unexpected data disclosure, authentication bypass, or error messages revealing query structure.

For Axum applications, middleBrick tests specific NoSQL injection patterns:

• Authentication bypass: { "username": "admin", "password": { "$ne": null } }
• ObjectId manipulation: Invalid ObjectIds or operators in path parameters
• Property traversal: Field names that could access unintended data
• JavaScript execution: $where operator with malicious code

The scanner's 12 parallel security checks include NoSQL injection testing that sends these payloads to your Axum endpoints and analyzes the responses. Unlike static analysis tools that might miss runtime context, middleBrick tests the actual deployed API surface.

For comprehensive coverage, combine middleBrick's black-box scanning with your own integration tests. Create test cases that attempt NoSQL injection on each endpoint and verify that user input is properly sanitized before database queries.

Remediating NoSQL injection in Axum applications requires a defense-in-depth approach. Start with input validation using Axum's type system and validation libraries.

For path parameters that should be MongoDB ObjectIds, use the bson::oid::ObjectId type with proper error handling:

use bson::oid::ObjectId;async fn get_user(db: Data<MongoDb>, Path(user_id): Path<String>) -> Result<Json<User>> {  // Safe: ObjectId validation rejects invalid input  let oid = match ObjectId::parse_str(&user_id) {    Ok(oid) => oid,    Err(_) => return Err(Error::bad_request("Invalid user ID"))  };  let query = doc! { "_id": oid };  let result = db.collection("users").find_one(query, None).await?;  // ...}

For query parameters and JSON bodies, use serde's validation features to restrict acceptable input. Define structs with specific types rather than generic strings:

#[derive(Deserialize)]struct SearchRequest {  #[serde(deserialize_with = "validate_field_name")]  field: String,  term: String,  // ...}fn validate_field_name<'de, D>(  deserializer: D) -> Result<String, D::Error>where D: serde::Deserializer<'de> {  let field = String::deserialize(deserializer)?;  // Allow only specific fields  let allowed = ["username", "email", "created_at"];  if !allowed.contains(&field.as_str()) {    return Err(serde::de::Error::custom("Invalid field"));  }  Ok(field)}

Implement parameterized queries using MongoDB's query builders rather than raw doc! macros when possible. While MongoDB doesn't support prepared statements like SQL, you can create safe query builders:

async fn safe_search(db: Data<MongoDb>, query: Json<SearchRequest>) -> Result<Json<Vec<Product>>> {  // Safe: controlled query construction  let mut builder = QueryBuilder::new();  builder.add_text_search(query.field.clone(), query.term.clone());  let mongo_query = builder.build();  let results = db.collection("products").find(mongo_query, None).await?;  // ...}

Consider using Axum's middleware for cross-cutting concerns like input sanitization. Create a middleware that validates and sanitizes requests before they reach route handlers:

use axum::{extract::Request, middleware::Next, response::Response};async fn sanitization_middleware(  mut req: Request<Body>,  next: Next<Body>) -> Result<Response> {  // Validate and sanitize request body  let (parts, body) = req.into_parts();  let bytes = hyper::body::to_bytes(body).await?;  let sanitized = sanitize_input(&bytes);  let new_req = Request::from_parts(parts, Body::from(sanitized));  next.run(new_req).await}

Finally, implement comprehensive logging and monitoring. Log suspicious query patterns and set up alerts for potential NoSQL injection attempts. Axum's structured logging middleware can help track query patterns and identify anomalies.