Broken Authentication in Buffalo with Dynamodb

Broken Authentication in a Buffalo application that uses DynamoDB as its persistence layer typically arises from a mismatch between session management in the framework and how identity data is stored and retrieved from DynamoDB. When authentication state is stored client-side (for example in a cookie or JWT) but the server-side session validation relies on incomplete or inconsistent data from DynamoDB, the boundary between framework logic and database becomes a risk surface.

Buffalo does not manage sessions in a database by default; it relies on secure, signed cookies to store session identifiers. If these cookies are not properly protected and the backend does not validate the session against a strongly-consistent, permission-bound DynamoDB table that enforces least-privilege access, an attacker can tamper with identifiers or escalate privileges. A common pattern is to store a user ID or a session handle in a cookie and then perform a GetItem or Query on a DynamoDB table keyed by that identifier. If the application does not verify ownership and scope using conditional checks, and if IAM policies for the DynamoDB client are overly permissive, an attacker can reuse another user’s session handle to access or modify data.

DynamoDB-specific misconfigurations amplify the risk. For example, using a global secondary index (GSI) without proper filtering can expose session records to enumeration. If partition keys are not carefully chosen to align with access patterns, a Scan operation might inadvertently return multiple user records, enabling horizontal privilege escalation across accounts. Additionally, missing encryption at rest or in transit, combined with weak IAM policies, can allow an attacker who has obtained low-privilege credentials to read or update authentication metadata. Real-world attack patterns such as credential theft leading to token replay intersect with DynamoDB access paths; if your application caches tokens or session metadata in DynamoDB without expirations or strong integrity checks, stolen credentials remain valid for longer windows.

Consider a scenario where a session table uses a composite key PK = USER#<user_id> and SK = SESSION#<session_id>. If the backend retrieves a session by session_id without verifying that the authenticated user owns that PK/SK pair, an attacker can craft a request with a known session_id belonging to another user. Because DynamoDB returns the item without an ownership assertion, Buffalo may treat the request as authenticated. This is a BOLA (Broken Object Level Authorization) pattern rooted in database access design. Furthermore, unauthenticated LLM endpoints or verbose error messages from DynamoDB (e.g., conditional check failures) can leak whether a session or user exists, aiding enumeration attacks covered under LLM/AI Security checks offered by tools that test such vectors.

Compliance mappings highlight the severity: OWASP API Top 10 A01:2023 (Broken Authentication), PCI-DSS requirements around identity verification, and SOC2 controls over access management all intersect here. Because DynamoDB stores the source of truth for identity, any weakness in how Buffalo reads and writes to it directly impacts authentication integrity. Using the middleBrick dashboard to track authentication-related findings across scans can help correlate these risks with runtime behavior, while the GitHub Action can gate merges if risk scores degrade due to insecure session handling or DynamoDB policy misconfigurations.

Remediation centers on ensuring that every read and write to DynamoDB is constrained by the authenticated subject, uses least-privilege IAM, and validates integrity before acting on the data. Below are concrete code examples for a Buffalo application using the AWS SDK for Go to interact with DynamoDB securely.

First, structure your DynamoDB table to enforce ownership at the key level. Use a partition key that includes the user identifier, and perform GetItem with a composite key derived from the authenticated session. This prevents horizontal escalation across users.

import (
	"context"
	"github.com/gobuffalo/buffalo"
	"github.com/aws/aws-sdk-go-v2/service/dynamodb"
	"github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
	"github.com/aws/aws-sdk-go-v2/aws"
)

func getSession(c buffalo.Context, client *dynamodb.Client, userID, sessionID string) (map[string]types.AttributeValue, error) {
	pk := "USER#" + userID
	sk := "SESSION#" + sessionID
	out, err := client.GetItem(context.TODO(), &dynamodb.GetItemInput{
		TableName: aws.String("sessions"),
		Key: map[string]types.AttributeValue{
			"PK": &types.AttributeValueMemberS{Value: pk},
			"SK": &types.AttributeValueMemberS{Value: sk},
		},
	})
	if err != nil {
		return nil, err
	}
	return out.Item, nil
}

This approach ensures that an attacker cannot supply a different user_id to access another user’s session, because the PK is derived from the authenticated user ID, not from user-controlled input alone. Always use conditional writes to enforce state integrity; for example, when creating or updating a session, assert that the item does not already exist or that an expected attribute matches.

func createSession(client *dynamodb.Client, userID, sessionID string, expiry int64) error {
	pk := "USER#" + userID
	sk := "SESSION#" + sessionID
	_, err := client.PutItem(context.TODO(), &dynamodb.PutItemInput{
		TableName: aws.String("sessions"),
		Item: map[string]types.AttributeValue{
			"PK": &types.AttributeValueMemberS{Value: pk},
			"SK": &types.AttributeValueMemberS{Value: sk},
			"ExpiresAt": &types.AttributeValueMemberN{Value: string(expiry)},
		},
		ConditionExpression: aws.String("attribute_not_exists(PK)"),
	})
	return err
}

For queries that need to list sessions or validate ownership, prefer a GSI with a partition key that aligns with user context, and apply filters that are enforced server-side. Avoid Scan operations on authentication tables; instead, design queries that retrieve a single item or a bounded set scoped to the user.

func listUserSessions(client *dynamodb.Client, userID string) ([]map[string]types.AttributeValue, error) {
	pk := "USER#" + userID
	out, err := client.Query(context.TODO(), &dynamodb.QueryInput{
		TableName:              aws.String("sessions"),
		IndexName:              aws.String("UserIdIndex"),
		KeyConditionExpression: aws.String("PK = :pk"),
		ExpressionAttributeValues: map[string]types.AttributeValue{
			":pk": &types.AttributeValueMemberS{Value: pk},
		},
	})
	if err != nil {
		return nil, err
	}
	return out.Items, nil
}

Ensure that the IAM role associated with your Buffalo application grants only the necessary DynamoDB actions (GetItem, PutItem, Query) on the specific table and index resources. Do not use wildcard resources. Combine this with secure cookie settings in Buffalo (Secure, HttpOnly, SameSite) to reduce the impact of session fixation or theft. Because middleBrick scans test authentication, BOLA/IDOR, and property authorization in parallel, running a scan after these changes will reflect improved risk posture and help you verify that remediation aligns with compliance frameworks such as OWASP API Top 10 and SOC2.

Finally, consider integrating the middleBrick CLI to automate verification: middlebrick scan <your-api-url> returns a score and findings that you can track over time via the Web Dashboard. In CI/CD, the GitHub Action can fail builds if authentication-related risk scores drop, while the MCP Server lets you scan APIs directly from your IDE as you develop these DynamoDB-backed endpoints.