Api Rate Abuse in Rocket with Firestore

Rate abuse in a Rocket API that uses Cloud Firestore as the backend can occur when request-rate controls are applied at the HTTP layer but not enforced at the Firestore operation level. Without per-user or per-key limits on document reads, writes, and queries, an authenticated or unauthenticated client can generate excessive Firestore operations within a short window. This pattern is common in endpoints that accept a resource identifier and perform a get() or collection().where() without validating the caller’s intended throughput.

Firestore’s native pricing and quota model charges per document read, write, and delete. If a Rocket handler maps 1:1 to Firestore operations, an attacker can amplify cost and resource consumption by issuing many requests that each trigger multiple Firestore reads or writes. For example, an endpoint that retrieves a user profile and then eagerly loads related documents in a loop can generate N + 1–style bursts that appear as legitimate traffic but consume disproportionate capacity.

The combination of Rocket’s async runtime and Firestore’s distributed nature can mask abuse until operational signals appear, such as elevated operation counts or throttling responses from Firestore. Because Firestore does not enforce application-level rate limits, protections must be implemented in the Rocket service itself. Effective controls include token-bucket or sliding-window rate limiters keyed by user ID or API key before any Firestore transaction, and validation that the caller is authorized for the targeted document paths.

Attack patterns to consider include rapid creation of short-lived documents intended to exhaust write quotas, and repeated queries that scan large collections to infer data presence or extract information through timing differences. These map onto the BFLA/Privilege Escalation and Excessive Agency checks in middleBrick’s 12 parallel security checks, especially when endpoints expose Firestore document references without enforcing ownership or scope boundaries.

middleBrick’s scan can surface these risks by correlating unauthenticated or low-identity test calls with Firestore-centric findings such as missing rate limiting and overly permissive read paths. The tool’s checks for Rate Limiting and BFLA/Privilege Escalation highlight where request volume controls should be enforced before Firestore operations, and where authorization checks must validate document-level permissions rather than relying on route parameters alone.

Remediation focuses on enforcing per-identity rate limits before any Firestore interaction and tightening authorization to the document level. Below are concrete patterns you can apply in a Rocket handler using the official Firestore Rust SDK (google-cloud-firestore) and common middleware crates.

1. Rate limiting with per-key tracking

Use a token-bucket implementation backed by a fast KV store (e.g., Redis). The key should include a user or API key so limits are enforced per principal. Only after the rate check passes should the handler proceed to Firestore.

use rocket::State;
use std::time::Duration;
use redis_async::Client;

async fn allow_request(user_key: &str, limiter: &State<Client>) -> bool {
    let mut conn = limiter.get_conn().await.unwrap();
    // Allow 30 requests per minute per user_key
    let limit: usize = 30;
    let current = redis::cmd("GET")
        .arg(format!("rate:{}", user_key))
        .query_async(&mut conn)
        .await
        .unwrap_or(0);
    if current >= limit {
        return false;
    }
    if current == 0 {
        redis::cmd("SET")
            .arg(format!("rate:{}", user_key))
            .arg(1)
            .arg("EX")
            .arg(60)
            .query_async(&mut conn)
            .await
            .unwrap();
    } else {
        redis::cmd("INCR")
            .arg(format!("rate:{}", user_key))
            .query_async(&mut conn)
            .await
            .unwrap();
    }
    true
}

2. Document-level ownership checks

Always scope Firestore queries to the requesting user. Avoid collection scans that depend only on client-supplied IDs. Use Firestore security-style semantics in your application logic by binding queries to the user ID.

use google_cloud_firestore::client::Client;
use google_cloud_firestore::model::Value;
use rocket::serde::json::Json;

async fn get_user_data(uid: String, requester_id: String, db: &Client) -> Result<Value, Box<dyn std::error::Error>> {
    // Ensure the requester is the owner of the data
    if uid != requester_id {
        return Err("Unauthorized".into());
    }
    let doc_path = format!("users/{}", uid);
    let doc = db.get_document(&doc_path, None).await?;
    Ok(doc)
}

3. Query cost control

Avoid unbounded queries and repeated subcollection reads. Prefer targeted document fetches and set reasonable page sizes. If you must list user-owned documents, use collection group queries with a limit and ensure the caller’s ID is part of the filter.

use google_cloud_firestore::client::Client;
use google_cloud_firestore::model::{StructuredQuery, Direction};

async fn list_user_items(uid: String, limit: i32, db: &Client) -> Result<Vec<Value>, Box<dyn std::error::Error>> {
    let query = StructuredQuery {
        from: vec![google_cloud_firestore::model::CollectionSelector {
            collection_id: "user_items".to_string(),
            ..Default::default()
        }],
        where_filter: Some(Box::new(google_cloud_firestore::model::Filter {
            field_filter: Some(Box::new(google_cloud_firestore::model::FieldFilter {
                field: google_cloud_firestore::model::FieldReference {
                    field_path: "user_id".to_string(),
                },
                op: google_cloud_firestore::model::PropertyFilterOp::Equal,
                value: Some(Value::StringValue(uid)),
            })),
            ..Default::default()
        })),
        order_by: vec![google_cloud_firestore::model::Order {
            field: google_cloud_firestore::model::FieldReference {
                field_path: "created_at".to_string(),
            },
            direction: Direction::Descending as i32,
        }],
        limit: Some(limit),
        ..Default::default()
    };
    let results = db.run_query(query, None).await?;
    let items: Vec<Value> = results.into_iter().filter_map(|r| r.to).collect();
    Ok(items)
}

Combine these patterns with middleware that extracts an identity (user ID or API key) and applies rate limiting before routing to handlers. This reduces Firestore load and aligns with best practices such as those referenced in middleBrick’s findings, where missing rate limiting and excessive document operations are flagged.