Spring4shell in Axum with Dynamodb

The Spring4shell vulnerability (CVE-2022-22965) affects applications using Spring MVC or Spring WebFlux with Data Binding, where an attacker can bypass intended type constraints via specially crafted payloads. In an Axum-based Rust service that deserializes JSON into structures which are later forwarded to an AWS DynamoDB client, the risk arises not from Axum itself but from how data is passed downstream to DynamoDB operations. If Axum handlers accept user input into generic key-value containers (e.g., HashMap) and forward those containers as expression attribute values to DynamoDB via the AWS SDK, an attacker may attempt to inject property references such as #{T(java.lang.Runtime).getRuntime().exec('id')}.

DynamoDB’s support for nested documents and condition expressions can inadvertently expose these injections if input is used in expression attribute names or values without strict validation. For example, a crafted payload in a JSON field mapped to a DynamoDB expression attribute name could attempt to reference class objects or invoke methods during expression parsing on the backend. Although DynamoDB does not execute arbitrary code, the combination with a vulnerable Java-based backend microservice (e.g., a Spring app in the request path) can amplify impact by enabling remote code execution before the data reaches DynamoDB.

When Axum routes requests to a Spring service via HTTP and that service uses DynamoDB as a backend, the attack surface spans both the HTTP layer and the database layer. An OpenAPI spec analyzed by middleBrick might reveal an endpoint like POST /users/{id} with a body schema that loosely maps to DynamoDB attribute names. If the spec uses $ref to share definitions and lacks strict validation, runtime probes can detect whether expression-like syntax is accepted. middleBrick’s LLM/AI Security checks include active prompt injection tests and system prompt leakage detection, which, while designed for LLM endpoints, underscore the importance of validating unexpected input patterns that could propagate to downstream systems such as DynamoDB.

In practice, this specific combination is risky when Axum applications deserialize JSON into structures that are not tightly bound to expected DynamoDB attribute schemas. Without rigorous input validation and schema enforcement, an attacker can probe for deserialization gadgets or expression language injection that may affect the service chain. middleBrick’s checks for Input Validation, Property Authorization, and SSRF help identify whether user-controlled data reaches DynamoDB expressions, while the Inventory Management and Unsafe Consumption checks highlight missing schema governance that could facilitate such attacks.

Remediation in this context focuses on strict schema binding, avoiding dynamic attribute names, and isolating downstream calls. Ensure Axum handlers validate and transform inputs into fixed structures before issuing DynamoDB requests. Use condition expressions with placeholder attribute names and supply values exclusively through expression attribute values, never as expression text. middleBrick’s per-category breakdowns, available in the Web Dashboard and CLI reports, can highlight insecure patterns and map findings to frameworks such as OWASP API Top 10 and SOC2.

To secure DynamoDB interactions from Axum, bind inputs to strongly typed structures and avoid constructing expression attribute names from user data. Use the official AWS SDK for Rust (aws-sdk-dynamodb) with explicit attribute containers, and validate all fields before building requests.

Example: safe DynamoDB put item with expression attribute values only

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;

async fn put_user_safe(client: &Client, user_id: &str, email: &str) -> Result<(), aws_sdk_dynamodb::Error> {
    let item = std::collections::HashMap::from([
        ("user_id".to_string(), AttributeValue::S(user_id.to_string())),
        ("email".to_string(), AttributeValue::S(email.to_string())),
        ("status".to_string(), AttributeValue::S("active".to_string())),
    ]);

    client.put_item()
        .table_name("users")
        .set_item(Some(item))
        .send()
        .await?;
    Ok(())
}

Example: using expression attribute values to avoid injection in condition checks

async fn update_user_email_if_empty(client: &Client, user_id: &str, new_email: &str) -> Result<(), aws_sdk_dynamodb::Error> {
    client.update_item()
        .table_name("users")
        .key("user_id", AttributeValue::S(user_id.to_string()))
        .update_expression("SET #e = :val")
        .condition_expression("attribute_not_exists(#e)")
        .expression_attribute_names([("#e", "email")])
        .expression_attribute_values([(":val", AttributeValue::S(new_email.to_string()))])
        .send()
        .await?;
    Ok(())
}

In Axum handlers, validate and map JSON bodies to concrete structs rather than generic maps:

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

#[derive(Debug, Deserialize, Serialize)]
struct CreateUser {
    user_id: String,
    email: String,
}

async fn create_user(Json(payload): Json<CreateUser>) -> Result {
    let client = todo::get_dynamo_client();
    put_user_safe(&client, &payload.user_id, &payload.email).await.map_err(|e| {
        (StatusCode::INTERNAL_SERVER_ERROR, e.to_string())
    })?;
    Ok(StatusCode::CREATED)
}

These patterns ensure DynamoDB expression syntax never incorporates untrusted input, mitigating injection risks that could be chained with vulnerable deserialization in dependent services. middleBrick’s CLI can scan your endpoints and provide JSON output to verify that such safe patterns are enforced, while the Web Dashboard tracks security scores over time. For teams using CI/CD, the GitHub Action can fail builds if risk scores drop below a chosen threshold, and the MCP Server allows scanning APIs directly from AI coding assistants within your IDE.