Broken Access Control in Actix with Dynamodb

Broken Access Control occurs when an Actix web service interacts with DynamoDB without enforcing authorization checks on a per-request basis. In this combination, the risk typically arises because application-level identity or context (for example, a user ID from an HTTP header or JWT) is used to form DynamoDB key conditions, but those conditions are not validated against the authenticated subject before the query runs.

Consider an endpoint like GET /users/{user_id}/profile. If the handler builds a DynamoDB GetItem key using user_id from the path but does not verify that the requesting user is the same user_id (or an admin), an IDOR/BOLA flaw exists. An attacker can change the path parameter to another user ID and retrieve profiles they should not see. Because DynamoDB does not enforce application-level permissions, the database will return the item if the key exists, and the Actix handler may return it without an authorization check, resulting in unauthorized data exposure.

Another common pattern is using DynamoDB Query with a partition key derived from the requester (e.g., user_id) but failing to ensure that the partition key value is bound from the authenticated subject rather than from an attacker-controlled input. For example, if the handler trusts a request parameter for the partition key instead of the authenticated subject, this can lead to BOLA/IDOR where one user enumerates another user’s data by guessing or iterating IDs. Insecure default behaviors in DynamoDB, such as permissive resource policies or missing fine-grained IAM conditions, can further widen the attack surface when combined with an Actix service that does not validate scope at the query level.

Real-world exploit patterns mirror the OWASP API Top 10 Broken Object Level Authorization category. For instance, an attacker sends modified HTTP requests to the Actix endpoint, manipulating the user_id path or query parameters. If the service constructs a DynamoDB GetItem or Query key using the attacker-supplied value, and the IAM role associated with the service has broad read permissions, the attacker can read other users’ data. In some cases, missing rate limiting and weak authentication further enable enumeration and brute-force attacks on user IDs.

Because middleBrick tests unauthenticated attack surfaces and includes BOLA/IDOR and Property Authorization checks, it can surface these misconfigurations by correlating runtime requests with DynamoDB key patterns found in OpenAPI specs. Findings include the absence of subject-to-key binding validation and overly broad permissions, both of which map to compliance frameworks such as OWASP API Top 10 and SOC2 controls. The scanner does not fix these issues; it provides findings with severity and remediation guidance to help developers tighten authorization in the Actix layer before data is exposed through DynamoDB queries.

Remediation centers on ensuring that every DynamoDB request is constrained to the data the requester is allowed to access. In Actix, this means deriving partition key values from authenticated identity (for example, from session or JWT claims) and never from unvalidated request parameters. Always enforce authorization before constructing the key condition, and use IAM policies with least privilege and conditional constraints.

Example: safe GetItem by authenticated subject in Actix with the AWS SDK for Rust (aws-sdk-dynamodb). The handler extracts the user ID from a JWT claim, uses it as the partition key, and avoids any path-based override.

use actix_web::{web, HttpResponse, Result};
use aws_sdk_dynamodb::Client;
use serde_json::json;

async fn get_user_profile(
    client: web::Data,
    claims: web::ReqData<Claims>, // JWT claims containing sub
) -> Result<HttpResponse> {
    let user_id = claims.sub.clone(); // subject-derived identifier
    let table_name = "users";

    let resp = client
        .get_item()
        .table_name(table_name)
        .key("user_id", aws_sdk_dynamodb::types::AttributeValue::S(user_id))
        .send()
        .await
        .map_err(|e| actix_web::error::ErrorInternalServerError(e))?;

    if let Some(item) = resp.item() {
        Ok(HttpResponse::Ok().json(item))
    } else {
        Ok(HttpResponse::NotFound().finish())
    }
}

Example: safe Query with partition key bound to subject and explicit attribute projection. This prevents IDOR by ensuring the query only touches the authenticated user’s items.

async fn list_user_orders(
    client: web::Data<Client>,
    claims: web::ReqData<Claims>,
) -> Result<HttpResponse> {
    let partition_key = format!("USER#{}", claims.sub);
    let resp = client
        .query()
        .table_name("orders")
        .key_condition_expression("pk = :pk")
        .expression_attribute_values(
            ":pk",
            aws_sdk_dynamodb::types::AttributeValue::S(partition_key),
        )
        .projection_expression("order_id, amount, created_at")
        .send()
        .await
        .map_err(|e| actix_web::error::ErrorInternalServerError(e))?;

    let items = resp.items().unwrap_or_default();
    Ok(HttpResponse::Ok().json(items))
}

Remediation also involves infrastructure and configuration: apply least-privilege IAM policies to the Actix service role so it can access only items where the partition key matches the authenticated subject’s identifier. Use DynamoDB condition expressions (e.g., attribute_exists or owner = :sub) as an additional safeguard at the database level. Combine these checks with robust authentication in Actix (validated tokens, secure session handling) and avoid exposing raw user input as keys. middleBrick’s Pro plan supports continuous monitoring and CI/CD integration, which can help detect regressions in authorization logic before deployment, while the CLI allows quick local scans to validate endpoint behavior.