Stack Overflow in Actix with Dynamodb

A Stack Overflow in an Actix web service that uses Dynamodb typically occurs when recursive or deeply nested data handling (for example, recursive JSON serialization of DynamoDB attribute values or recursive traversal of nested map/list structures) consumes the call stack until the runtime terminates or the process becomes unresponsive. In this combination, the risk is not only application crashes but also exposure of internal state or amplification of request rates when nested item attributes are not bounded.

Actix-web is a high-performance, actor-based Rust framework. When you integrate it with Dynamodb via the AWS SDK for Rust (aws-sdk-dynamodb), each request may perform multiple SDK calls that deserialize strongly-typed responses into Rust structs. If your deserialization logic or business logic recursively processes deeply nested maps or lists—common when storing hierarchical or graph-like data in DynamoDB—each level of nesting can add frames to the call stack. An attacker can supply crafted input that increases nesting depth beyond what your code expects, leading to a stack overflow that consumes memory and CPU, degrading availability.

Moreover, because DynamoDB responses can include variable-length attribute lists and nested structures, unchecked recursion in business logic can interact poorly with Actix’s asynchronous runtime. A recursive function that processes each nested map without depth limits can starve the Actix runtime of capacity, leading to timeouts or service instability. This is especially dangerous when endpoints are publicly exposed, because an unauthenticated request can trigger deep traversal paths that amplify resource usage.

In the context of the LLM/AI Security checks provided by middleBrick, this class of vulnerability is surfaced as an Unsafe Consumption or Input Validation finding: the scanner sends payloads designed to probe deeply nested structures and observes whether the runtime or process destabilizes. Because DynamoDB’s flexible schema enables arbitrarily nested data, developers must enforce strict depth and size limits on deserialized items and avoid recursive processing in hot paths.

To remediate Stack Overflow risks when using Dynamodb with Actix, enforce bounded recursion, validate input depth before processing, and prefer iterative algorithms over recursive ones. Below are concrete, realistic code examples using the official AWS SDK for Rust (aws-sdk-dynamodb) and Actix-web.

1. Validate and bound nesting depth before deserialization

Before converting a DynamoDB AttributeValue to your domain model, inspect maps and lists recursively with a depth counter. Reject or truncate items that exceed a safe threshold.

use aws_sdk_dynamodb::types::AttributeValue;

const MAX_DEPTH: usize = 8;

fn validate_depth(value: &AttributeValue, depth: usize) -> Result<(), String> {
    if depth > MAX_DEPTH {
        return Err(format!("Nesting depth exceeded limit of {MAX_DEPTH}"));
    }
    match value {
        AttributeValue::M(map) => {
            for (_, v) in map {
                validate_depth(v, depth + 1)?;
            }
        }
        AttributeValue::L(list) => {
            for item in list {
                validate_depth(item, depth + 1)?;
            }
        }
        _ => {}
    }
    Ok(())
}

// Example usage inside an Actix handler:
async fn get_item_handler(
    client: web::Data<aws_sdk_dynamodb::Client>,
    req: web::Json<GetItemRequest>,
) -> Result<HttpResponse, Error> {
    let response = client
        .get_item()
        .table_name(&req.table)
        .set_key(Some(req.key.clone()))
        .send()
        .await?;

    if let Some(item) = response.item {
        validate_depth(&AttributeValue::from(item.clone()), 0)?;
        // safe to process further
    }
    Ok(HttpResponse::Ok().finish())
}

2. Use iterative traversal instead of recursion

Replace recursive functions with an explicit stack to control memory usage and avoid unbounded call stacks.

use aws_sdk_dynamodb::types::AttributeValue;
use std::collections::VecDeque;

fn iterate_items_iteratively(root: &AttributeValue) -> Vec<String> {
    let mut result = Vec::new();
    let mut stack = VecDeque::new();
    stack.push_back(root);

    while let Some(current) = stack.pop_front() {
        match current {
            AttributeValue::M(map) => {
                for (_, v) in map {
                    stack.push_back(v);
                }
            }
            AttributeValue::L(list) => {
                for item in list {
                    stack.push_back(item);
                }
            }
            AttributeValue::S(s) => result.push(s.clone()),
            _ => {}
        }
    }
    result
}

3. Configure AWS SDK timeouts and limits

Set reasonable timeouts and buffer limits on the SDK client used by Actix to prevent long-running or resource-intensive operations from stalling the runtime.

use aws_config::BehaviorVersion;
use aws_sdk_dynamodb::config::{Config, Region};
use aws_sdk_dynamodb::Client;
use std::time::Duration;

let config = Config::builder()
    .region(Region::new("us-east-1"))
    .timeout_config(
        aws_sdk_dynamodb::config::TimeoutConfig::builder()
            .operation_timeout(Duration::from_secs(5))
            .operation_attempt_timeout(Duration::from_secs(2))
            .build()
    )
    .build();
let client = Client::from_conf(config);

4. Enforce size limits on request and response payloads in Actix

Use Actix’s payload limits to prevent large DynamoDB responses from flooding memory and triggering deep processing paths.

use actix_web::middleware::NormalizePath;
use actix_web::{web, App, HttpResponse, HttpServer, Responder};

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    HttpServer::new(|| {
        App::new()
            .wrap(NormalizePath::trim())
            .app_data(web::JsonConfig::default().limit(4096)) // limit payload size
            .route("/scan", web::post().to(scan_handler))
    })
    .bind("127.0.0.1:8080")?
    .run()
    .await
}

5. Prefer paginated and filtered reads

When scanning or querying DynamoDB, use ProjectionExpression and Limit to reduce the size and depth of returned items. This minimizes the data your Actix handlers must process recursively.

let response = client
    .scan()
    .table_name("users")
    .limit(100)
    .projection_expression("id, name, settings") // shallow subset
    .send()
    .await?;

By combining input validation, iterative traversal, SDK timeouts, payload limits, and selective attribute projection, you can safely consume Dynamodb responses in Actix without risking Stack Overflow or denial of availability.