Information Disclosure in Actix with Dynamodb

When building Actix web services that interact with DynamoDB, information disclosure risks arise from a mismatch between Actix’s request handling patterns and DynamoDB’s data access behavior. In this combination, developers may inadvertently expose sensitive data through misconfigured endpoints, insufficient input validation, or improper error handling that reveals internal identifiers or item structures.

Actix routes often bind path or query parameters directly to handler functions. If these parameters map to DynamoDB key attributes without validation, an attacker can manipulate identifiers to access items that should be private. For example, changing an integer ID in a URL may expose other users’ records if the application does not enforce ownership checks after retrieving the item from DynamoDB. Because DynamoDB returns no data when a key does not exist, an attacker can infer the existence of valid identifiers by observing differences in HTTP status codes or response times, leading to user enumeration or account harvesting.

Another vector involves DynamoDB’s attribute structure. If you serialize entire DynamoDB item responses into JSON for the client, you might expose internal metadata such as aws:repodriver fields, version attributes, or internal state flags that were never intended for external consumption. In Actix, this commonly occurs when using generic serialization like serde_json::to_string on a HashMap or a struct that reflects the raw DynamoDB response. Over-permissive CORS settings in Actix can further allow cross-origin leakage of these responses to unauthorized origins, making sensitive data accessible to scripts running in the browser.

The scan’s Input Validation and Data Exposure checks highlight these risks by testing path traversal, IDOR patterns, and response content for sensitive fields. For instance, submitting a modified primary key may return a 200 OK with another user’s data, indicating a broken authorization check rather than a missing item. Because the scan runs black-box and references the OWASP API Top 10 category for Broken Object Level Authorization, it flags cases where DynamoDB key manipulation in Actix endpoints leads to unauthorized data exposure.

Additionally, error messages from the DynamoDB client, such as conditional check failures or provisioned throughput errors, may be returned verbatim to the client in Actix responses. These messages can reveal table names, index names, or internal partition key structures, aiding an attacker in reconnaissance. The LLM/AI Security checks in middleBrick specifically look for system prompt leakage and output patterns containing API keys or structured secrets that might be echoed from backend logic or misconfigured logging.

To detect these issues without authentication, middleBrick tests unauthenticated endpoints that interact with DynamoDB, looking for inconsistent behaviors such as different response lengths for valid versus invalid IDs, verbose error payloads, or JSON responses containing fields like user_email or internal_id that should be restricted. Proper remediation requires both server-side validation in Actix and disciplined data modeling in DynamoDB to ensure least-privilege access and minimal data exposure.

Remediation focuses on strict input validation, ownership verification, and safe serialization when using DynamoDB with Actix. Below are concrete, working examples that you can apply to reduce information disclosure.

1. Validate and scope DynamoDB key access

Never trust path parameters as direct DynamoDB keys. Resolve the authenticated user’s identity server-side and ensure the requested key matches the user’s scope.

use actix_web::{web, HttpResponse, Result};
use aws_sdk_dynamodb::Client;
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct PathParams {
    user_id: String,
}

async fn get_user_data(
    params: web::Path<PathParams>,
    user_auth: Option<UserSession>, // your auth extractor
    client: web::Data<Client>,
) -> Result<HttpResponse> {
    let auth_user = user_auth.ok_or_else(|| HttpResponse::Unauthorized().finish())?;
    // Ensure the requested user_id matches the authenticated user
    if auth_user.id != params.user_id {
        return Ok(HttpResponse::Forbidden().body("Access denied"));
    }

    let resp = client
        .get_item()
        .table_name("users")
        .key("user_id", aws_sdk_dynamodb::types::AttributeValue::S(params.user_id.clone()))
        .send()
        .await?;

    if let Some(item) = resp.item {
        // Explicitly select safe fields instead of returning the full item
        let safe_user = SafeUser::from_dynamodb_item(&item)?;
        Ok(HttpResponse::Ok().json(safe_user))
    } else {
        Ok(HttpResponse::NotFound().body("User not found"))
    }
}

#[derive(Serialize)]
struct SafeUser {
    user_id: String,
    display_name: String,
}

impl SafeUser {
    fn from_dynamodb_item(item: &std::collections::HashMap<String, aws_sdk_dynamodb::types::AttributeValue>) -> Result<Self, actix_web::Error> {
        Ok(SafeUser {
            user_id: item.get("user_id").and_then(|v| v.as_s().ok()).map(|s| s.to_string()).ok_or_else(|| actix_web::error::ErrorBadRequest("missing user_id"))?,
            display_name: item.get("display_name").and_then(|v| v.as_s().ok()).map(|s| s.to_string()).ok_or_else(|| actix_web::error::ErrorBadRequest("missing display_name"))?,
        })
    }
}

2. Avoid leaking DynamoDB metadata in responses

When serializing responses, exclude internal attributes and prefer explicit structs over raw HashMap serialization.

use serde_json::json;

// Instead of returning the raw item:
// let json = serde_json::to_string(&item)?; // risky

// Use a controlled output:
let output = json!({
    "user_id": item.get("user_id").and_then(|v| v.as_s()).map(|s| s.to_string()),
    "email": item.get("email").and_then(|v| v.as_s()).map(|s| s.to_string()),
    // do not include internal fields like 'created_at_ms', 'version', or 'aws:*'
});
Ok(HttpResponse::Ok().json(output));

3. Use conditional expressions safely and avoid verbose errors

Handle ConditionalCheckFailedException without exposing internals, and avoid echoing DynamoDB error details to the client.

use aws_sdk_dynamodb::types::SdkError;
use aws_sdk_dynamodb::operation::get_item::GetItemError;

async fn safe_update(
    client: &Client,
    key: &str,
    value: i64,
) -> Result<(), actix_web::Error> {
    let resp = client
        .update_item()
        .table_name("settings")
        .key("setting_key", AttributeValue::S(key.to_string()))
        .update_expression("set #val = :v")
        .expression_attribute_names({"#val".to_string(): "value"})
        .expression_attribute_values({:v".to_string(): AttributeValue::N(value.to_string())})
        .send()
        .await;

    match resp {
        Ok(_) => Ok(()),
        Err(SdkError::ServiceError { err, .. }) if err.is_conditional_check_failed_exception() => {
            // Log internally for debugging, but return a generic message
            log::warn!(target: "dynamodb", "Conditional check failed for key: {}", key);
            Err(actix_web::error::ErrorConflict("Update conflict"))
        }
        Err(e) => {
            log::error!(target: "dynamodb", "Unexpected DynamoDB error: {:?}", e);
            Err(actix_web::error::ErrorInternalServerError("Service error"))
        }
    }
}

4. Enforce least-privilege in DynamoDB and Actix CORS

Configure CORS in Actix to restrict origins, and ensure your IAM policy for DynamoDB grants only the necessary actions on specific resources. This reduces the impact of any potential misconfiguration that could lead to information disclosure.

5. Scan with middleBrick to validate fixes

Use the middleBrick CLI to verify that your changes reduce the risk of information disclosure:

middlebrick scan https://api.yourservice.com/users/123

Compare the before and after reports to ensure that sensitive fields are no longer exposed in unauthenticated responses and that IDOR indicators are cleared.