Injection Flaws in Axum with Firestore

Injection flaws in an Axum service that uses Google Cloud Firestore typically arise when data from HTTP requests is incorporated into Firestore queries without proper validation or parameterization. Axum, a web framework for Rust, encourages strongly typed extractors and handlers, but developers can still build dynamic queries by concatenating user input into Firestore paths, collection names, or filter values. Because Firestore does not support parameterized queries in the same way a relational database does, constructing query constraints from unchecked input can lead to query manipulation that exposes more data than intended or allows reads/writes to unexpected documents.

For example, consider an endpoint that retrieves a user profile using a user ID taken directly from the request path or query parameters. If the handler uses this identifier to build a Firestore document path such as format!("users/{}", user_id) and then fetches the document, an attacker could supply IDs like ../../other_users/abc123 or encoded equivalents to traverse paths and access data belonging to other users. This pattern maps to the broader BOLA/IDOR category and can be discovered by scanning Axum endpoints that interact with Firestore without strict path validation. Similarly, dynamic collection names derived from request data can lead to unintended access across collections, effectively bypassing intended data isolation boundaries.

Another scenario involves filter values used in Firestore where clauses. If a handler builds a query by interpolating strings into field paths or operator values, an attacker may supply crafted input to change query semantics or trigger errors that reveal internal structure. While Firestore itself enforces strict schema rules and access controls at the database level, the application layer in Axum must ensure that any input used to construct queries is validated, whitelisted, or transformed into safe references before being passed to Firestore client methods. Without these safeguards, the unauthenticated attack surface includes endpoints that expose data through overly permissive read rules or misconfigured security rules that the application query logic assumes are enforced.

In an Axum + Firestore stack, insecure defaults or incomplete validation compound the risk. For instance, accepting JSON payloads with numeric or string fields and directly inserting them into Firestore document updates without type checks can lead to Property Authorization issues or unexpected data exposure. Because the framework does not inherently enforce constraints on Firestore operations, developers must explicitly design handlers to bind input to known document IDs or use server-side identifiers only. MiddleBrick’s LLM/AI Security checks can detect endpoints that accept user-controlled input and form Firestore queries or document paths dynamically, flagging potential injection surfaces before they are exploited in production.

Overall, injection risks in Axum with Firestore stem from treating Firestore paths and query constraints as templates rather than strict interfaces. The framework provides extractors and routing primitives, but secure integration requires rigorous input validation, canonical path construction, and strict separation between user-supplied data and Firestore identifiers. Regular scanning with a tool that exercises these endpoints in a black-box manner, such as MiddleBrick’s unauthenticated tests, helps surface these classes of issues early in the development lifecycle.

To remediate injection risks in Axum when working with Firestore, always validate and sanitize inputs before constructing document paths or query constraints. Prefer using hardcoded or server-derived identifiers rather than concatenating user input into paths. Below are concrete examples that demonstrate secure patterns for common Axum handler scenarios.

Secure document retrieval with explicit ID validation

Instead of building document paths from raw user input, validate the ID against an allowlist or a strict pattern, then use it as a single path segment. For example:

use axum::{routing::get, Router};
use google_cloud_firestore::client::Client;
use std::net::SocketAddr;

async fn get_user_profile(
    Path(user_id): Path<String>,
    client: Extension<Arc<Client>>
) -> Result<impl IntoResponse, (StatusCode, String)> {
    // Validate: only allow alphanumeric user IDs of fixed length
    if !user_id.chars().all(|c| c.is_ascii_alphanumeric()) || user_id.len() != 24 {
        return Err((StatusCode::BAD_REQUEST, "Invalid user identifier".to_string()));
    }
    let doc_path = format!("users/{}", user_id);
    let document = client.doc(&doc_path).get().await.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    // handle response
    Ok(Json(document))
}

This approach ensures that the user-supplied identifier cannot be used to traverse paths or inject additional segments, mitigating BOLA/IDOR risks associated with path manipulation.

Parameterized queries and strict field filtering

When querying collections, avoid constructing field paths or operator values from user input. Use enums or constants to map allowed fields and operators, and bind user values only as document field values in filter predicates:

use google_cloud_firestore::{client::Client, query::FilterCondition};

async fn list_public_posts(
    Query(params): Query<HashMap<String, String>>,
    client: Extension<Arc<Client>>
) -> Result<Json<Vec<FirestoreDocument>>, (StatusCode, String)> {
    let allowed_fields = ["category", "status"];
    let field = params.get("field").ok_or_else(|| (StatusCode::BAD_REQUEST, "missing field".to_string()))?;
    if !allowed_fields.contains(&field.as_str()) {
        return Err((StatusCode::BAD_REQUEST, "invalid field".to_string()));
    }
    let value = params.get("value").ok_or_else(|| (StatusCode::BAD_REQUEST, "missing value".to_string()))?;
    let query = client.collection("posts")
        .add_filter(field.as_str(), FilterCondition::Equal, value.clone());
    let results = query.get().await.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(Json(results))
}

This pattern prevents attackers from altering which field is queried or which operator is used, reducing the risk of data exposure or logic bypass via query manipulation.

Document updates with typed structures

For document updates, prefer strongly typed structures and avoid dynamically setting keys based on user input. If dynamic updates are necessary, validate each key against a known set and ensure values conform to expected types:

use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize, Serialize)]
struct ProfileUpdate {
    display_name: Option<String>,
    email: Option<String>,
    // do not accept arbitrary keys from user
}

async fn update_profile(
    Path(user_id): Path<String>,
    Json(payload): Json<ProfileUpdate>,
    client: Extension<Arc<Client>>
) -> Result<impl IntoResponse, (StatusCode, String)> {
    let doc_path = format!("users/{}", user_id);
    let updates: HashMap<String, serde_json::Value> = serde_json::to_value(payload)
        .map_err(|_| (StatusCode::BAD_REQUEST, "invalid payload".to_string()))?
        .as_object().ok_or_else(|| (StatusCode::BAD_REQUEST, "expected object".to_string()))?
        .iter()
        .map(|(k, v)| (k.clone(), v.clone()))
        .collect();
    client.doc(&doc_path).update(updates).await.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(StatusCode::NO_CONTENT)
}

By limiting which fields can be updated and avoiding raw key insertion, you reduce the attack surface for injection-style manipulations against Firestore documents.

These practices align with secure coding guidance and help ensure that Axum handlers interact with Firestore in a way that minimizes injection risks. MiddleBrick scans can validate whether these mitigations are present by testing endpoints that interact with Firestore and verifying that user input does not directly shape query structure.