Insecure Design in Buffalo with Dynamodb

Insecure design in a Buffalo application that uses Amazon DynamoDB typically arises from how data access patterns, authorization checks, and schema choices are modeled before implementation. When authorization logic is handled primarily at the application layer without complementary constraints in DynamoDB, it is possible for an insecure design to allow horizontal privilege escalation or excessive data exposure.

Consider a multi-tenant API where each organization’s records are identified by an org_id partition key. If endpoints rely only on user-supplied parameters (e.g., /orgs/{org_id}/members) and the backend constructs DynamoDB queries using that parameter directly, the design may lack verification that the authenticated user belongs to the requested org_id. This matches the BOLA/IDOR category within middleBrick’s 12 checks, which tests for lack of ownership validation across resources. Even without authentication on the endpoint, an unauthenticated attacker could iterate over plausible org_id values and observe differences in response behavior or data volume, leading to information exposure.

Another insecure pattern is over-permissive write paths. If the application accepts incoming JSON and constructs DynamoDB expression attribute values without strict type and length validation, an attacker can inject unexpected expressions or oversized attributes. This can contribute to data exposure and input validation findings because the design fails to constrain item sizes or sanitize reserved keywords. For instance, using attribute names derived from user input without aliasing can cause accidental overwrites or interpretation of expression names, a concern aligned with Property Authorization checks.

DynamoDB’s schema-less nature amplifies insecure design risks when combined with Buffalo’s convention-driven routing and form binding. If models bind request payloads directly to DynamoDB attribute names, malformed or malicious payloads can map to unexpected keys, bypassing intended validation layers. middleBrick’s Input Validation and Property Authorization checks surface these issues by comparing runtime behavior against the OpenAPI specification and expected authorization boundaries. Additionally, missing rate-limiting designs can allow cost exploitation via repeated scans or queries, which the LLM/AI Security probes may flag as excessive automated requests targeting unauthenticated endpoints.

To illustrate, an insecure handler might look like this, where orgID is taken directly from the URL and used to build a query without confirming membership:

// Example of an insecure design pattern in Buffalo
func ListMembers(c buffalo.Context) error {
    orgID := c.Params.Get("org_id")
    svc := dynamodb.NewFromConfig(cfg)
    out, err := svc.Query(c, &dynamodb.QueryInput{
        TableName: aws.String("OrganizationMembers"),
        KeyConditionExpression: aws.String("org_id = :v"),
        ExpressionAttributeValues: map[string]types.AttributeValue{
            ":v": &types.AttributeValueMemberS{Value: orgID},
        },
    })
    if err != nil {
        return c.Error(http.StatusInternalServerError, err)
    }
    return c.Render(http.StatusOK, r.JSON{Members: out.Items})
}

This design does not verify that the current user is a member of orgID, illustrating an insecure design that exposes BOLA/IDOR. middleBrick’s scan would report this under BOLA/IDOR and Property Authorization, with remediation guidance to enforce ownership checks and apply tighter authorization at the data access layer.

Remediation focuses on ensuring that every DynamoDB interaction is paired with explicit authorization checks and constrained input handling. The following patterns align with secure design principles and are detectable as improvements by middleBrick’s checks.

First, enforce ownership or tenant membership before constructing queries. Retrieve the authenticated subject’s allowed organizations from a separate authorization store (e.g., a relational table or cached permissions), and intersect the requested org_id with that set.

// Secure pattern with tenant ownership check
func ListMembers(c buffalo.Context) error {
    orgID := c.Params.Get("org_id")
    userID := c.Session().Get("user_id")
    if userID == nil {
        return c.Unauthorized()
    }

    // Fetch allowedOrgs for the user from a permissions table or cache
    allowedOrgs, err := getAllowedOrganizations(userID.(string))
    if err != nil || !contains(allowedOrgs, orgID) {
        return c.Forbidden()
    }

    svc := dynamodb.NewFromConfig(cfg)
    out, err := svc.Query(c, &dynamodb.QueryInput{
        TableName: aws.String("OrganizationMembers"),
        KeyConditionExpression: aws.String("org_id = :org"),
        FilterExpression: aws.String("user_id = :usr"),
        ExpressionAttributeValues: map[string]types.AttributeValue{
            ":org": &types.AttributeValueMemberS{Value: orgID},
            ":usr": &types.AttributeValueMemberS{Value: userID.(string)},
        },
    })
    if err != nil {
        return c.Error(http.StatusInternalServerError, err)
    }
    return c.Render(http.StatusOK, r.JSON{Members: out.Items})
}

Second, validate and constrain inputs to mitigate injection and data exposure risks. Use allowlists for sort or filter parameters, enforce size limits on strings, and avoid passing raw user input into expression names. When constructing expression attribute names, prefer static aliases mapped via ExpressionAttributeNames.
// Secure input validation and expression name handling
func QueryItems(c buffalo.Context) error {
    filter := c.QueryParam("filter")
    sort := c.QueryParam("sort")

    // Allowlist validation
    allowedSort := map[string]bool{"created_at": true, "name": true}
    if !allowedSort[sort] {
        sort = "created_at" // default
    }

    svc := dynamodb.NewFromConfig(cfg)
    out, err := svc.Query(c, &dynamodb.QueryInput{
        TableName: aws.String("Items"),
        IndexName: aws.String("GSI_Sort"),
        KeyConditionExpression: aws.String("pk = :pk AND begins_with(sort_key, :sk)"),
        ExpressionAttributeNames: map[string]string{
            "#nm": "name",
        },
        ExpressionAttributeValues: map[string]types.AttributeValue{
            ":pk": &types.AttributeValueMemberS{Value: "ITEMS#"},
            ":sk": &types.AttributeValueMemberS{Value: sort + "#"},
            ":filter": &types.AttributeValueMemberS{Value: filter},
        },
    })
    if err != nil {
        return c.Error(http.StatusInternalServerError, err)
    }
    return c.Render(http.StatusOK, r.JSON{Items: out.Items})
}

Third, apply defense-in-depth by enabling DynamoDB Server-Side Encryption with customer-managed keys where compliance requires it, and design your table schema with sparse indexes and TTL to reduce unnecessary data exposure. These operational design choices lower data exposure findings in middleBrick scans and align with Encryption and Data Exposure checks.
Finally, integrate middleBrick into your workflow to validate that these fixes reduce risk. Use the CLI to test changes locally:
middlebrick scan https://api.example.com/openapi.json

Or, add the GitHub Action to fail builds if the score drops below your chosen threshold, ensuring insecure designs are caught before deployment.