Memory Leak in Aspnet with Dynamodb

A memory leak in an ASP.NET application that uses the AWS SDK for .NET with DynamoDB occurs when objects related to DynamoDB operations are held in memory longer than necessary. This typically involves undisposed or long-lived references to AmazonDynamoDBClient, DocumentModel entities, or ScanCondition/query objects, and the associated AsyncResponse pipeline buffers. Even without credentials, middleBrick can detect indicators such as unusually high heap retention patterns during repeated unauthenticated scans, flagging potential resource exhaustion as a high-severity finding in the Data Exposure or Unsafe Consumption checks.

In ASP.NET, common patterns that lead to leaks with DynamoDB include:

• Creating a single static AmazonDynamoDBClient without proper lifetime management. While reusing a client is recommended, attaching it to long-lived request objects or capturing it in closures can keep response streams and internal buffers alive.
• Using FromQueryAsync or ScanAsync without consuming or disposing the Search object. The SDK returns a paginated enumerable; if you do not iterate to completion or call GetNextSetAsync in a using context, internal pagination buffers and HTTP response content may not be released promptly.
• Storing DynamoDB entity models (POCOs mapped via DynamoDBContext) in static caches without eviction policies. These objects can hold references to internal marshalling metadata and unmanaged buffers, increasing GC pressure and delaying collection.
• Neglecting cancellation tokens and timeouts on long-running queries. If a query is cancelled late or not cancelled at all, the underlying HTTP handler may retain buffers until finalization, which middleBrick can correlate with rate limiting and data exposure findings.

Because middleBrick tests unauthenticated attack surfaces, it can surface symptoms of resource strain by observing inconsistent response behavior across repeated endpoint calls, without needing to inspect internal memory. Findings related to Input Validation and Rate Limiting may indirectly highlight patterns that exacerbate retention issues, while the Inventory Management check can identify missing client disposal practices in reported configurations.

Remediation focuses on correct client lifetime, proper disposal of SDK resources, and avoiding static retention of heavy objects. Use dependency injection with appropriate lifetimes, ensure paginated results are fully consumed within a using block, and avoid caching entity instances that embed internal buffers.

Example 1: Configure the DynamoDB client with a transient or singleton lifetime (prefer singleton) and use it safely across requests without capturing context.

// Program.cs or Startup.cs
builder.Services.AddAWSService<IAmazonDynamoDB>();
builder.Services.AddSingleton<IAmazonDynamoDB, AmazonDynamoDBClient>();

Example 2: Use DynamoDBContext with a scoped or transient lifetime and dispose resources explicitly when using low-level operations.

using Amazon.DynamoDBv2;
using Amazon.DynamoDBv2.DataModel;
using Amazon.DynamoDBv2.Model;

public class DynamoDbService : IAsyncDisposable
{
    private readonly IAmazonDynamoDB _client;
    private readonly DynamoDBContext _context;

    public DynamoDbService(IAmazonDynamoDB client)
    {
        _client = client;
        _context = new DynamoDBContext(_client);
    }

    public async Task<T> LoadByIdAsync<T>(string id, CancellationToken ct = default)
    {
        return await _context.LoadAsync<T>(id, ct);
    }

    public async IAsyncEnumerable<Product> ScanProducts([EnumeratorCancellation] CancellationToken ct = default)
    {
        var config = new DynamoDBOperationConfig
        {
            Limit = 100,
            CancellationToken = ct
        };
        var search = _context.FromScanAsync<Product>(new List<ScanCondition>(), config);
        await foreach (var page in search.GetNextSetAsync(ct))
        {
            foreach (var item in page)
            {
                yield return item;
            }
        }
    }

    public async ValueTask DisposeAsync()
    {
        if (_context is IAsyncDisposable asyncDisposableContext)
            await asyncDisposableContext.DisposeAsync();
        if (_client is IAsyncDisposable asyncDisposableClient)
            await asyncDisposableClient.DisposeAsync();
        else
            _client.Dispose();
    }
}

Example 3: Fully consume paginated results from a low-level Scan and release buffers immediately.

using Amazon.DynamoDBv2;
using Amazon.DynamoDBv2.Model;

public class LowLevelService
{
    private readonly IAmazonDynamoDB _client;

    public LowLevelService(IAmazonDynamoDB client) => _client = client;

    public async Task ScanAndDiscardAsync(string tableName, CancellationToken ct = default)
    {
        var request = new ScanRequest
        {
            TableName = tableName,
            Limit = 1000
        };
        ScanResponse response;
        do
        {
            response = await _client.ScanAsync(request, ct);
            // Explicitly process or discard items to avoid holding references
            foreach (var item in response.Items)
            {
                // Process item or ignore if not needed
            }
            request.ExclusiveStartKey = response.LastEvaluatedKey;
        } while (!response.LastEvaluatedKey.IsNullOrEmpty());

        // Release response resources as soon as possible
        response.Dispose();
    }
}

Example 4: Avoid static caching of entity instances; prefer DTOs or snapshots.

private static readonly Dictionary<string, Product> _staticCache = new();

// Instead of caching DynamoDBEntity objects directly, map to lightweight DTOs
public ProductDto GetProductDto(string id)
{
    var entity = _context.LoadAsync<ProductEntity>(id).Result;
    return new ProductDto { Id = entity.Id, Name = entity.Name, Price = entity.Price };
}

By applying these patterns, you reduce the risk of holding unmanaged buffers and long-lived references. middleBrick can then verify that the endpoint demonstrates stable memory-related behavior across repeated scans, lowering findings in the Unsafe Consumption and Data Exposure categories.