Broken Authentication in Spring Boot with Dynamodb

Broken Authentication in a Spring Boot application using Amazon DynamoDB often stems from how identity is modeled, validated, and cached rather than from DynamoDB itself. When authentication logic relies on DynamoDB as the primary identity store without enforcing strict access controls and token hygiene, several classes of vulnerabilities can appear.

One common pattern is storing user credentials or password digests in a DynamoDB table keyed by username or email. If the application does not use strong hashing (e.g., bcrypt or PBKDF2) and does not enforce secure password policies, attackers can obtain or brute-force credentials. A second risk arises when session or token metadata (such as refresh tokens, one-time codes, or temporary credentials) is stored in DynamoDB with weak access controls. Because DynamoDB access is governed by IAM policies, overly permissive table policies or misconfigured IAM roles can allow one user to read or update another’s token record, leading to horizontal or vertical privilege escalation.

Spring Boot applications often integrate DynamoDB via the AWS SDK or an object mapping library. If the integration does not enforce least-privilege IAM roles for each request, a compromised service role or over-scoped credentials can allow an attacker to query or modify any item in the table. This maps to the BOLA/IDOR category when endpoints accept user-supplied identifiers (e.g., userId) and use those identifiers directly in DynamoDB key expressions without verifying that the authenticated principal is allowed to access that identifier. For example, an endpoint like /api/users/{userId} that constructs a GetItem request using the path parameter as the partition key can be abused to enumerate or access other users’ data if the caller’s identity is not rechecked against the item’s owning partition key.

Another vector specific to this stack is weak or missing rate limiting on authentication endpoints. DynamoDB has generous provisioned throughput or on-demand capacity, but if Spring Boot does not enforce per-user or per-IP rate limits on login or token refresh endpoints, attackers can perform credential stuffing or brute-force attacks without triggering backend throttling. This is an instance of the Rate Limiting check in middleBrick’s 12 checks, where missing controls enable rapid, automated attacks against authentication surfaces.

Additionally, caching layers (e.g., Spring Cache backed by DynamoDB or in-memory caches that store authentication tokens) can inadvertently expose sensitive data if entries are shared across users or if cache keys include identifiers that are not scoped to a tenant or user. Improperly configured cache TTLs can also keep valid sessions alive longer than intended, increasing the window for session hijacking. middleBrick’s Authentication and BOLA/IDOR checks are designed to surface these logic flaws by testing unauthenticated and authenticated contexts to confirm that access controls are enforced consistently.

Remediation focuses on strict access checks, secure credential storage, and scoping data access to the authenticated principal. Below are concrete, working examples using the AWS SDK for Java v2 with Spring Boot and DynamoDB.

Secure user lookup and ownership check

Always derive the DynamoDB key from the authenticated name rather than trusting request parameters. Use Spring Security’s Authentication to obtain the current user name and scope all DynamoDB operations to that identity.

import software.amazon.awssdk.services.dynamodb.DynamoDbClient;
import software.amazon.awssdk.services.dynamodb.model.GetItemRequest;
import software.amazon.awssdk.services.dynamodb.model.GetItemResponse;
import org.springframework.security.core.Authentication;
import org.springframework.stereotype.Service;

@Service
public class UserProfileService {

    private final DynamoDbClient ddb;
    private final String tableName = System.getenv("USERS_TABLE");

    public UserProfileService(DynamoDbClient ddb) {
        this.ddb = ddb;
    }

    public GetItemResponse getUserProfileForCurrentUser(Authentication authentication) {
        String username = authentication.getName(); // e.g., authenticated principal
        GetItemRequest req = GetItemRequest.builder()
                .tableName(tableName)
                .key(Map.of("pk", AttributeValue.builder().s("USER#" + username).build()))
                .build();
        return ddb.getItem(req);
    }
}

This pattern ensures that the item requested maps directly to the authenticated principal, mitigating BOLA/IDOR. Never use request-supplied identifiers to form keys without re-confirming ownership.

Secure password storage and verification

Store password digests using a strong adaptive hash. Use Spring Security’s PasswordEncoder and configure it to use bcrypt. Avoid storing reversible secrets or low-entropy hashes in DynamoDB.

import org.springframework.security.crypto.bcrypt.BCryptPasswordEncoder;
import org.springframework.security.crypto.password.PasswordEncoder;
import org.springframework.stereotype.Service;
import java.util.Map;
import software.amazon.awssdk.services.dynamodb.DynamoDbClient;
import software.amazon.awssdk.services.dynamodb.model.PutItemRequest;
import software.amazon.awssdk.services.dynamodb.model.AttributeValue;

@Service
public class RegistrationService {

    private final DynamoDbClient ddb;
    private final PasswordEncoder encoder = new BCryptPasswordEncoder();
    private final String tableName = System.getenv("USERS_TABLE");

    public void register(String username, String rawPassword) {
        String hash = encoder.encode(rawPassword);
        PutItemRequest req = PutItemRequest.builder()
                .tableName(tableName)
                .item(Map.of(
                    "pk", AttributeValue.builder().s("USER#" + username).build(),
                    "password_hash", AttributeValue.builder().s(hash).build()
                ))
                .build();
        ddb.putItem(req);
    }

    public boolean verify(String username, String rawPassword) {
        GetItemRequest req = GetItemRequest.builder()
                .tableName(tableName)
                .key(Map.of("pk", AttributeValue.builder().s("USER#" + username).build()))
                .build();
        String stored = ddb.getItem(req).item().get("password_hash").s();
        return encoder.matches(rawPassword, stored);
    }
}

Ensure the DynamoDB table enforces encryption at rest and that IAM policies grant the application role only GetItem/PutItem on the specific key condition (username principal). This aligns with the principle of least privilege and reduces the impact of credential exposure.

Rate limiting on authentication endpoints

Spring Boot applications should enforce rate limits to mitigate brute-force and credential stuffing. Use a token-bucket or fixed-window algorithm with a distributed cache to coordinate limits across instances.

import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.stereotype.Component;

@Component
public class LoginRateLimiter {

    private final StringRedisTemplate redis;

    public LoginRateLimiter(StringRedisTemplate redis) {
        this.redis = redis;
    }

    public boolean allowAttempt(String username) {
        String key = "rate:login:" + username;
        Long current = redis.opsForValue().increment(key);
        if (current == 1) {
            redis.expire(key, java.time.Duration.ofMinutes(15));
        }
        return current != null && current <= 10; // allow 10 attempts per 15 minutes
    }
}

Call allowAttempt before processing credentials and return HTTP 429 when denied. This addresses the Rate Limiting check and reduces automated attack surface against authentication flows.