Api Rate Abuse in Spring Boot with Dynamodb

Rate abuse occurs when an attacker issues a high volume of requests to a Spring Boot endpoint that reads from or writes to DynamoDB, consuming backend capacity without effective controls. In this stack, the risk is amplified because DynamoDB has configurable read/write capacity units and burst behavior, and Spring Boot services often rely on the AWS SDK for data access. Without explicit rate limiting at the API layer, a single unauthenticated or weakly authenticated endpoint can be targeted to exhaust provisioned throughput, cause throttling exceptions (ProvisionedThroughputExceededException), increase latency, or trigger auto-scaling events that affect cost and availability.

The vulnerability surface exists across multiple dimensions:

• API endpoint design: Public or weakly protected endpoints (e.g., GET /users/{id}, /search, or admin endpoints) that directly invoke DynamoDB via the SDK can be hammered if rate limiting is absent or misconfigured.
• DynamoDB behavior: DynamoDB does not enforce request-rate limits at the table level for unauthenticated callers; it enforces capacity limits. Sustained high request rates can exceed provisioned read/write capacity or burst credits, resulting in HTTP 400/429 responses and degraded performance for legitimate users.
• Spring Boot integration patterns: Common patterns such as RestTemplate or WebClient calls to DynamoDB via Data Access Objects (DAOs) without concurrency limits, request deduplication, or token-bucket controls can create hotspots. For example, an endpoint that queries a DynamoDB table by partition key without caching or request coalescing can magnify the load on the table under abuse.

Real-world attack patterns include rapid calls to an unauthenticated item lookup endpoint to exhaust read capacity units (RCUs), or repeated POST calls that consume write capacity units (WCUs). These map to OWASP API Top 10:2023 — API1:2023 Broken Object Level Authorization and API2:2023 Broken Authentication when rate controls are missing, and can also affect SOC 2 and GDPR expectations around availability and integrity.

middleBrick detects this risk by performing unauthenticated scans against Spring Boot endpoints that interact with DynamoDB, testing for missing or insufficient rate limiting across 12 parallel security checks including Rate Limiting and Unsafe Consumption. Reports include severity ratings and remediation guidance, helping teams identify where DynamoDB-backed endpoints require protection before they can be abused.

To mitigate rate abuse, implement a layered approach: enforce request-rate limits at the API gateway or within Spring Boot, use DynamoDB client-side controls, and design data access patterns that reduce hot partitions and excessive calls.

1) Rate limiting in Spring Boot

Use a token-bucket or fixed-window algorithm at the controller or filter level. A simple, production-friendly approach uses Spring Cache with a custom rate limiter per key (e.g., by API key or IP). Below is an example using Spring Boot, Guava RateLimiter, and a WebFilter to protect DynamoDB-driven endpoints:

@Component
public class RateLimitFilter implements WebFilter {

    private final LoadingCache rateLimiters = CacheBuilder.newBuilder()
        .maximumSize(10_000L)
        .build(new CacheLoader<String, RateLimiter>() {
            @Override
            public RateLimiter load(String key) {
                // 10 requests per second per key as an example
                return RateLimiter.create(10.0);
            }
        });

    @Override
    public Mono<Void> filter(ServerWebExchange exchange, WebFilterChain chain) {
        String key = exchange.getRequest().getRemoteAddress().getAddress().getHostAddress();
        RateLimiter limiter = rateLimiters.getUnchecked(key);
        if (limiter.tryAcquire()) {
            return chain.filter(exchange);
        } else {
            exchange.getResponse().setStatusCode(HttpStatus.TOO_MANY_REQUESTS);
            return exchange.getResponse().setComplete();
        }
    }
}

DynamoDbClient client = DynamoDbClient.builder()
    .region(Region.US_EAST_1)
    .overrideConfiguration(ClientOverrideConfiguration.builder()
        .retryPolicy(RetryPolicy.builder()
            .numRetries(3)
            .backoffStrategy(jitterBackoff())
            .build())
        .build())
    .build();

private static SdkBackoffStrategy jitterBackoff() {
    return SdkBackoffStrategy.fullJitterBackoff(Duration.ofMillis(50), Duration.ofSeconds(20));
}

@Service
public class UserRepository {

    private final DynamoDbEnhancedClient enhancedClient;

    public UserRepository(DynamoDbEnhancedClient enhancedClient) {
        this.enhancedClient = enhancedClient;
    }

    public Optional findById(String userId) {
        DynamoDbTable<User> table = enhancedClient.table("Users", TableSchema.fromBean(User.class));
        try {
            return table.getItem(Key.builder().partitionValue(userId).build());
        } catch (ProvisionedThroughputExceededException e) {
            // handle throttling, log, and optionally return a degraded response
            throw new ApiRateLimitException("Table throughput exceeded", e);
        }
    }
}