Sandbox Escape in Axum with Dynamodb

A sandbox escape in the context of an Axum service that uses DynamoDB occurs when an attacker is able to move from a constrained execution context (for example, a Lambda function with a narrowly scoped IAM role) to a broader set of AWS resources. DynamoDB becomes an effective pivot because it is often reachable from the application environment and may contain sensitive data or serve as a channel for data exfiltration. Even though DynamoDB itself is a managed service and not a traditional network perimeter, misconfigured resource policies, overly permissive IAM, and unsafe deserialization or query-building patterns in Axum code can allow an attacker to issue unauthorized operations against DynamoDB tables that the application itself legitimately accesses.

In Axum, this typically surfaces through one or more of these patterns: constructing DynamoDB clients or requests from user-controlled input without strict validation, using dynamic table names or key condition expressions derived from request parameters, or deserializing untrusted data into structures that influence which AWS SDK operations are invoked. An attacker may supply crafted JSON or form values that change the target table, the key schema, or the filter expressions, effectively turning the application into an unauthorized proxy for DynamoDB operations. Because Axum is a Rust web framework, the danger often lies in unsafe use of serde and runtime request building rather than a language-level sandbox bypass, but the outcome is the same: the effective scope of access escapes the intended boundaries.

Consider an endpoint that accepts a table name and a partition key value to fetch an item. If the code does not strictly validate or whitelist the table name, an attacker can request a different table that holds more sensitive data. Even when the IAM role attached to the service has broader permissions, the intended design is to interact only with a specific table; the escape here is logical, not infrastructural, and DynamoDB reflects the widened impact. Similarly, if key condition expressions are built by string concatenation using user input without strict schema checks, an attacker may supply values that cause the query to scan a larger set of items or use a different index, effectively bypassing intended data segregation. These patterns map to common API security concerns such as Broken Object Level Authorization (BOLA) and Injection, and they are surfaced by middleBrick scans that test unauthenticated attack surfaces and check for insecure deserialization, input validation, and property authorization across the API surface, including DynamoDB interactions.

middleBrick detects these risks by correlating OpenAPI/Swagger specifications (including $ref resolution) with runtime behavior, flagging endpoints that accept resource identifiers that influence AWS SDK calls, and identifying missing authorization checks around DynamoDB operations. It does not fix the configuration or code, but it provides prioritized findings with severity ratings and remediation guidance so teams can tighten input validation, enforce table-name whitelists, and ensure IAM policies follow least privilege. By combining specification analysis with active probes, the scanner reveals how an otherwise contained API surface can be leveraged to reach beyond its intended scope when DynamoDB is involved.

Remediation focuses on strict input validation, fixed resource identifiers, and least-privilege IAM usage. In Axum, implement strong path and query extraction with validation layers before constructing any DynamoDB request. Do not allow user input to directly name tables or dictate key expressions. Use enums or constants to map allowed logical operations to specific tables and key schemas, and validate that the supplied identifiers match expected patterns.

Below is a concrete Rust example using the official AWS SDK for Rust (aws-sdk-dynamodb) with Axum. The handler validates the table name against a whitelist, uses a strongly typed structure for the partition key, and builds the request without concatenating raw strings for key values.

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;
use axum::{extract::Query, routing::get, Json, Router};
use serde::Deserialize;
use std::net::SocketAddr;

#[derive(Deserialize)]
struct ItemQuery {
    table: String,
    pk: String,
    sk: String,
}

async fn get_item_handler(
    client: axum::extract::State,
    Query(params): Query,
) -> Json> {
    // Whitelist of allowed tables; do not use user input as table name.
    const ALLOWED_TABLES: &[&str] = &["app_users", "app_metadata"];
    if !ALLOWED_TABLES.contains(¶ms.table.as_str()) {
        return Json(None);
    }

    let table_name = params.table;
    let key = aws_sdk_dynamodb::types::Key {
        pk: AttributeValue::S(params.pk),
        sk: AttributeValue::S(params.sk),
    };

    let output = client
        .get_item()
        .table_name(table_name)
        .set_key(Some(key))
        .send()
        .await
        .ok()
        .and_then(|resp| resp.item);

    Json(output)
}

#[tokio::main]
async fn main() {
    let config = aws_config::load_from_env().await;
    let client = Client::new(&config);
    let app = Router::new()
        .route("/item", get(get_item_handler))
        .with_state(client);

    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .await
        .unwrap();
}

This approach ensures that the table identity is controlled by the service, not by the request. For key values, treat them as opaque strings bound to the known schema rather than constructing dynamic expressions. If filtering across multiple tables is required, route the request internally to the correct table identifier instead of forwarding the table name from the client. Additionally, configure IAM roles for the runtime environment with permissions scoped strictly to the allowed tables and required operations (e.g., dynamodb:GetItem), avoiding wildcard actions. middleBrick’s scans can verify that endpoints do not reflect untrusted input into AWS SDK calls and that proper authorization checks are present, helping teams confirm that remediation is effective in practice.