Insecure Deserialization in Axum with Dynamodb

Insecure deserialization occurs when an application processes untrusted serialized data without sufficient validation. In an Axum application that uses DynamoDB, this risk arises when your API accepts serialized objects (for example via JSON payloads, MessagePack, or custom binary formats) and then deserializes them before storing or retrieving items from DynamoDB.

Consider an endpoint that accepts a serialized user profile or document, deserializes it with bincode, serde_cbor, or another format, and then writes it into a DynamoDB attribute. If the deserialization logic does not enforce strict type constraints and schema validation, an attacker can craft payloads that instantiate unexpected types, trigger side effects during deserialization, or overwrite sensitive fields. Axum’s extractor patterns (e.g., Json<T>, TypedHeader) make it straightforward to bind request bodies to structs, but if you use untyped or loosely typed deserialization to interact with DynamoDB item formats, you may inadvertently allow attacker-controlled data to dictate behavior beyond the intended data fields.

DynamoDB itself does not execute deserialization on the server side for standard item operations; the risk is carried into your application layer. An attacker might exploit insecure deserialization to bypass intended access controls (e.g., changing the item’s owner ID or partition key value during deserialization), escalate privilege by injecting crafted objects, or achieve injection via maliciously shaped data that leads to unexpected behavior when the item is later read and used. Since Axum often serves HTTP APIs that directly map to database operations, the path from an HTTP request through deserialization to DynamoDB read/write can become a channel for tampering if input validation and schema enforcement are weak.

Real-world patterns also include Lambda functions or backend workers that read DynamoDB streams and deserialize items for further processing. If those functions apply permissive deserialization to stream records, they may be exposed to similar injection or tampering. In all these cases, the vulnerability is not in DynamoDB but in how Axum handles and trusts deserialized input before persisting or retrieving items.

Remediation focuses on strict schema validation, avoiding generic deserialization of untrusted data, and ensuring that DynamoDB interactions use well-defined, typed models. Below are concrete Axum examples that show a vulnerable approach and a hardened approach.

Vulnerable pattern: permissive deserialization before DynamoDB put

// DO NOT DO THIS: permissive deserialization of untrusted data
use axum::{routing::post, Json, Router};
use serde::{Deserialize, Serialize};
use aws_sdk_dynamodb::Client;
use serde_json::Value;

#[derive(Deserialize, Serialize)]
struct UserProfile {
    user_id: String,
    email: String,
    // Dangerous: additional fields allowed
    #[serde(flatten)]
    extra: Value,
}

async fn create_user_handler(
    Json(payload): Json, // Accept any JSON without schema binding
    client: &State,
) -> Result {
    // Insecure: directly converting untrusted JSON into DynamoDB item with minimal checks
    let item = serde_json::to_string(&payload).map_err(|e| (StatusCode::BAD_REQUEST, e.to_string()))?;
    let _ = client.put_item()
        .table_name("UserProfiles")
        .set_item(Some(serde_json::from_str(&item).map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?))
        .send()
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(StatusCode::OK)
}

Hardened pattern: strict structs and validated field mapping for DynamoDB

use axum::{routing::post, Json, Router, extract::State};
use serde::{Deserialize, Serialize};
use aws_sdk_dynamodb::Client;
use aws_sdk_dynamodb::types::AttributeValue;
use std::collections::HashMap;

#[derive(Debug, Deserialize, Serialize)]
struct CreateUser {
    user_id: String,
    email: String,
    // Explicitly forbid extra fields
    #[serde(deny_unknown_fields)]
    _deny: (),
}

fn user_to_dynamodb(item: CreateUser) -> HashMap {
    let mut map = HashMap::new();
    map.insert("user_id".to_string(), AttributeValue::S(item.user_id));
    map.insert("email".to_string(), AttributeValue::S(item.email));
    map
}

async fn create_user_handler(
    Json(payload): Json, // Strictly bound to validated struct
    client: &State,
) -> Result {
    let item = user_to_dynamodb(payload);
    client.put_item()
        .table_name("UserProfiles")
        .set_item(Some(item))
        .send()
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(StatusCode::CREATED)
}

Remediation for DynamoDB stream consumers

If your Axum service also consumes DynamoDB streams, apply the same strict deserialization discipline. Define explicit event structs and avoid deserializing raw record data into generic Value or serde_json::Map unless you have validated and constrained the schema. This prevents malicious stream records from triggering unexpected logic when deserialized.

General measures

• Use strongly typed structs with #[serde(deny_unknown_fields)] when binding request payloads that map to DynamoDB items.
• Validate all identifiers and keys before using them in DynamoDB operations to ensure they conform to expected patterns (e.g., UUID format, length limits).
• Do not accept serialized blobs of arbitrary user-defined types for deserialization; prefer explicit field mapping or safe formats with schema enforcement.
• Combine these practices with middleBrick scans to detect insecure deserialization patterns and insecure direct object references (BOLA/IDOR) that may be exposed through DynamoDB-backed endpoints.