Uninitialized Memory in Axum with Dynamodb

Uninitialized memory is a class of vulnerability where a program uses memory contents that were never explicitly set before those contents are exposed to downstream systems or stored. In an Axum application that interacts with DynamoDB, the risk emerges at the intersection of Rust’s safety guarantees, serialization behavior, and the expectations of the DynamoDB wire format.

When deserializing HTTP inputs into Rust structs, developers commonly use strongly-typed extractors such as Json<T>. If the target struct contains fields of primitive numeric or string types that are not explicitly provided in the JSON payload, those fields will hold indeterminate values rather than zero-initialized data, because Rust does not implicitly zero-initialize struct fields derived from deserialization. This indeterminate state can propagate into DynamoDB operations when the application constructs request parameters from that struct. For example, a field intended to represent an optional numeric attribute might carry an uninitialized value that is not semantically meaningful; if the application passes this field into a DynamoDB UpdateItem expression or conditional check, the operation may act on an unintended value, bypassing logical constraints that would normally prevent unsafe writes.

DynamoDB itself does not understand Rust memory semantics. When an Axum service builds a DynamoDB request using an uninitialized field, the raw bytes representing that value are sent to the service. Depending on the encoding, this can produce unexpected filter conditions or update expressions. In the context of authorization checks such as BOLA/IDOR, an uninitialized numeric key might coincidentally match another user’s record due to how conditional operators evaluate unexpected values, leading to privilege escalation or information exposure. Similarly, during inventory management or property authorization checks, uninitialized attributes may incorrectly pass validation logic, causing the service to expose or mutate data that should be restricted.

Moreover, serialization to JSON for logging or for transmission to DynamoDB attribute values can expose uninitialized memory contents. If an Axum handler logs the deserialized struct before validation, sensitive runtime artifacts such as stack pointers or residual heap data might be recorded. When these logs are later analyzed or when structured logs are correlated with DynamoDB streams, indirect leakage of uninitialized content can occur. This aligns with the LLM/AI Security checks supported by middleBrick, which scan for unintended data exposure in outputs; similar care is required for structured data pipelines involving DynamoDB.

Real-world mappings to known standards reinforce the impact. Uninitialized memory in this context can violate conditions assumed by OWASP API Top 10 A01:2023 broken object level authorization, where missing or malformed identifiers permit unauthorized access. It can also affect compliance expectations mapped by PCI-DSS and SOC2 around integrity controls. middleBrick’s scanning approach, which includes BOLA/IDOR and Property Authorization checks alongside Data Exposure analysis, is designed to surface these classes of findings with prioritized remediation guidance.

Remediation focuses on ensuring all data flowing into DynamoDB operations is explicitly initialized and validated within Axum handlers. The primary strategy is to avoid relying on default struct initialization for business-critical fields and to enforce presence checks before constructing DynamoDB expressions.

1. Use Option<T> for optional fields and validate before use.

use axum::extract::Json;
use aws_sdk_dynamodb::types::AttributeValue;
use serde::{Deserialize, Serialize};

#[derive(Deserialize, Serialize)]
struct UpdateItemInput {
    user_id: String,
    #[serde(default)]
    score: Option<i32>,
}

async fn update_item_handler(
    Json(payload): Json<UpdateItemInput>,
    client: aws_sdk_dynamodb::Client,
) -> Result<(), (axum::http::StatusCode, String)> {
    let user_id = payload.user_id;
    let score = match payload.score {
        Some(s) => AttributeValue::N(s.to_string()),
        None => return Err((axum::http::StatusCode::BAD_REQUEST, "score is required".into())),
    };

    client
        .update_item()
        .table_name("Items")
        .key("user_id", AttributeValue::S(user_id))
        .update_expression("set #s = :val")
        .expression_attribute_names("#s", "score")
        .expression_attribute_values(":val", score)
        .send()
        .await
        .map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    Ok(())
}

This pattern ensures that score is explicitly present and initialized before being converted to a DynamoDB attribute, preventing indeterminate values from reaching the database layer.

2. Apply strict validation for numeric ranges and string formats before constructing requests.

use validator::Validate;

#[derive(Deserialize, Validate)]
struct CreateItemInput {
    #[validate(length(min = 1))]
    name: String,
    #[validate(range(min = 0, max = 1000))]
    quantity: i32,
}

async fn create_item_handler(
    Json(payload): Json<CreateItemInput>,
) -> Result<impl IntoResponse, (axum::http::StatusCode, String)> {
    payload.validate().map_err(|e| {
        (axum::http::StatusCode::BAD_REQUEST, format!("Validation failed: {:?}", e))
    })?;

    let item = aws_sdk_dynamodb::types::PutItemInput::builder()
        .table_name("Inventory")
        .set_item(Some({
            let mut map = std::collections::HashMap::new();
            map.insert("name".to_string(), AttributeValue::S(payload.name));
            map.insert("quantity".to_string(), AttributeValue::N(payload.quantity.to_string()));
            map
        }))
        .build()
        .map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    // proceed with DynamoDB call
    Ok(())
}

By combining explicit optionality and validation, you ensure that only properly initialized values are used in DynamoDB condition expressions, reducing the risk of authorization bypasses related to BOLA/IDOR and property authorization checks.

3. Centralize request building to avoid scattered initialization errors. Implement a service layer that converts validated Axum payloads into DynamoDB attribute maps, ensuring every path initializes fields consistently. middleBrick’s scans, including the Inventory Management and Property Authorization checks, can highlight inconsistencies in how attributes are set before being used in API operations.

These practices align with secure coding guidance around handling uninitialized states and help ensure that integrations with DynamoDB remain robust against subtle memory-state issues that may not be evident during local testing but manifest in production under specific timing or input conditions.