Insecure Design in Fiber with Dynamodb

Insecure Design in a Fiber API that uses DynamoDB often arises from modeling data for convenience and access speed while neglecting authorization boundaries and validation constraints. A common pattern is a single DynamoDB table keyed by a partition such as PK = USER#<user_id> and a sort key like SK = POST#<post_id>. When endpoints are designed without scoping every request to the requester’s identity, an Insecure Design (a BOLA/IDOR finding in middleBrick’s checks) emerges: the API path accepts a resource identifier (e.g., /posts/:id) and directly queries DynamoDB for that ID without confirming that the item’s PK or SK belongs to the caller. Because the scan is unauthenticated, middleBrick can observe that an endpoint returns data for arbitrary IDs, exposing one user’s data to another.

A second dimension is missing or weak Item/Attribute-Level Authorization. For example, a “soft delete” flag stored as attribute_status may be checked only at the application layer after a DynamoDB GetItem, but the scan may find that the query itself does not enforce status constraints. This can lead to Data Exposure: sensitive attributes (PII, internal state) are returned even when they should be hidden. middleBullet’s Data Exposure check can surface this when responses include fields that should be conditional on user context or tenant boundaries.

A third dimension is over-permissive write paths and lack of input validation. If the API accepts a raw JSON payload and performs a DynamoDB PutItem or UpdateItem without whitelisting attributes, an attacker can inject or overwrite metadata used for authorization (e.g., admin=true), or exploit type confusion to bypass checks. middleBrick’s Input Validation and Property Authorization checks examine whether the schema constrains fields, prevents unexpected keys, and rejects malformed types. Without strict validation, an Insecure Design allows privilege escalation or injection into DynamoDB expressions, which may lead to BFLA or privilege escalation findings.

Concrete DynamoDB examples in Fiber that illustrate these risks:

• Unsafe direct key construction from user input:

// Insecure: pk/sk built from user input without scoping to requester
pk := fmt.Sprintf("POST#%s", id)
item, err := svc.GetItem(ctx, &dynamodb.GetItemInput{
    TableName: aws.String("Posts"),
    Key: map[string]types.AttributeValue{
        "PK": &types.AttributeValueMemberS{Value: pk},
        "SK": &types.AttributeValueMemberS{Value: sk},
    },
})

• Missing authorization check before query:

// Insecure: no check that the post belongs to the caller's tenant/user
var req PostRequest
if c.Bind(&req) != nil { ... }
key := map[string]types.AttributeValue{
    "PK": &types.AttributeValueMemberS{Value: fmt.Sprintf("POST#%s", req.ID)},
    "SK": &types.AttributeValueMemberS{Value: "METADATA"},
}
out, _ := svc.GetItem(ctx, &dynamodb.GetItemInput{TableName: aws.String("Posts"), Key: key})

These patterns align with OWASP API Top 10:2023’s Broken Object Level Authorization and Data Exposure, and map to compliance frameworks such as PCI-DSS and SOC2. middleBrick’s LLM/AI Security checks are less relevant here, but its 12 parallel scans will highlight the insecure design by correlating the spec (OpenAPI paths and DynamoDB key patterns) with runtime behavior.

Remediation centers on enforcing tenant and user boundaries at the DynamoDB layer, validating inputs, and tightening attribute exposure. Always derive partition and sort keys from the authenticated subject so queries are naturally scoped. For a Fiber endpoint, include the user identifier from the auth context (e.g., JWT) in the key construction, and avoid passing raw IDs from the client into key expressions without verification.

Concrete secure DynamoDB code examples for Fiber:

• Scoped read with user context:

// Secure: pk/sk include user scope; ID from client is used only as sort key suffix
userID := c.Locals("userID").(string) // from auth middleware
postID := c.Params("id")
pk := fmt.Sprintf("USER#%s", userID)
sk := fmt.Sprintf("POST#%s", postID)

item, err := svc.GetItem(ctx, &dynamodb.GetItemInput{
    TableName: aws.String("Posts"),
    Key: map[string]types.AttributeValue{
        "PK": &types.AttributeValueMemberS{Value: pk},
        "SK": &types.AttributeValueMemberS{Value: sk},
    },
})
if err != nil {
    c.Status(http.StatusInternalServerError).JSON(H{"error": "unable to fetch post"})
    return
}
if item.Item == nil {
    c.Status(http.StatusNotFound).JSON(H{"error": "post not found"})
    return
}
// Optionally filter attributes before response to avoid Data Exposure
resp := map[string]interface{}{
    "id":  postID,
    "msg": item.Item["message"]],
}
c.JSON(http.StatusOK, resp)

• Write with whitelisted attributes and validation:

// Secure: validate input shape and allow only safe fields
type CreatePostReq struct {
    Message string `json:"message" validate:"required,max=1000"`
    Tags    []string `json:"tags" validate:"dive,alphanum"`
}
var req CreatePostReq
if c.Bind(&req) != nil || validate.Struct(req) != nil {
    c.Status(http.StatusBadRequest).JSON(H{"error": "invalid payload"})
    return
}

userID := c.Locals("userID").(string)
pk := fmt.Sprintf("USER#%s", userID)
sk := "METADATA"

// Explicitly construct item to avoid injection of unexpected attributes
item := map[string]types.AttributeValue{
    "PK": &types.AttributeValueMemberS{Value: pk},
    "SK": &types.AttributeValueMemberS{Value: sk},
    "message": &types.AttributeValueMemberS{Value: req.Message},
    "tags": &types.AttributeValueMemberSS{Value: req.Tags},
    "created_at": &types.AttributeValueMemberS{Value: time.Now().UTC().Format(time.RFC3339)},
}
_, err := svc.PutItem(ctx, &dynamodb.PutItemInput{
    TableName: aws.String("Posts"),
    Item:      item,
})

• Enforce attribute-level controls on responses:

// After fetch, remove sensitive attributes to mitigate Data Exposure
safeItem := make(map[string]interface{})
for k, v := range item.Item {
    switch k {
    case "message", "tags", "created_at":
        if v != nil {
            safeItem[k] = v
        }
    // omit admin flags, internal metadata, PII
    }
}

Defensive patterns like these align with the Secure by Design approach and will improve scores on middleBrick’s checks for Authorization, Input Validation, Data Exposure, and Property Authorization. When combined with the Dashboard for tracking and the CLI for repeatable scans, teams can verify that fixes reduce the risk grade over time.