Insecure Design in Fiber with Mongodb

Insecure Design in a Fiber API that uses Mongodb often arises from how endpoints are structured and how data access is delegated to the database without adequate constraints. When route parameters or query values are directly interpreted as database selectors, the design can unintentionally expose sensitive records or enable enumeration. For example, mapping an id URL parameter directly into a Mongodb filter without validation or ownership checks can lead to Insecure Direct Object References (IDOR) and Broken Object Level Authorization (BOLA).

Another design risk occurs when business logic relies on client-supplied fields to determine access. If an endpoint uses a request body or query string to decide which project or organization a user can interact with, and the server does not re-derive permissions from the authenticated identity, the API surface becomes over-permissive. This pattern is common in tutorials that copy documents by inserting user input into findOneAndUpdate or find without scoping to a tenant or owner field. The combination of Fiber’s lightweight routing and Mongodb’s flexible query syntax makes it easy to construct endpoints that appear to work but silently bypass authorization.

Design issues also appear in how responses are shaped. Returning full Mongodb documents, including internal fields such as __v or hashed values, can leak sensitive data when endpoints do not explicitly project only needed fields. Additionally, if endpoints accept updates via $set on nested paths without validating which fields are mutable, clients can modify administrative flags or version counters. These decisions are architectural: they stem from trusting path and query data, insufficient scoping, and missing output filtering. Because the API remains unauthenticated during initial scans, middleBrick can detect these insecure design patterns by correlating OpenAPI paths that accept user-controlled parameters with runtime behavior that exposes or modifies data without ownership verification.

Real-world attack patterns mirror these design gaps. For instance, an endpoint like GET /users/:id that passes :id into collection.FindOne(ctx.Params("id")) without confirming the authenticated user owns that ID mirrors CVE-like scenarios where horizontal privilege escalation occurs. Similarly, using user-controlled query parameters to build bson.M filters can enable data exposure across tenant boundaries. middleBrick’s LLM and security checks highlight such issues by testing unauthenticated endpoints and observing whether responses contain data belonging to other users or roles, then mapping findings to OWASP API Top 10 and relevant compliance frameworks.

To secure a Fiber API using Mongodb, enforce ownership and scoping at the database layer and validate all inputs before building queries. Use context authentication to derive the subject ID, and embed it directly in filters instead of trusting URL or body parameters. The following patterns illustrate secure design.

1. Scope queries to the authenticated user

Always include an owner or user identifier in your query. If you store the user ID in the document, require it in every filter.

// Secure: scope to authenticated user ID
userID := ctx.Locals("userID").(string)
var result bson.M
err = collection.FindOne(ctx, bson.M{
    "_id":  userID,
    "role":"admin",
}).Decode(&result)
if err != nil {
    // handle not found
}

2. Validate and convert IDs before querying

Do not pass raw URL parameters directly to Mongodb. Parse and validate ObjectID first.

// Validate ID before using in a filter
id := ctx.Params("id")
objID, err := primitive.ObjectIDFromHex(id)
if err != nil {
    ctx.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid id"})
    return
}
err = collection.FindOne(ctx, bson.M{"_id": objID}).Decode(&document)
// handle error

3. Use whitelisted projection to avoid leaking fields

Explicitly include only required fields to prevent exposing metadata or sensitive values.

// Return only safe fields
err = collection.FindOne(ctx,
    bson.M{"_id": objID},
    options.FindOne().SetProjection(bson.M{"name":1, "email":1, "_id":1}),
).Decode(&userProfile)

4. Restrict mutable fields in updates

Validate which fields can be modified and avoid passing raw $set content from clients.

// Allow only specific fields to be updated
allowed := map[string]bool{"displayName": true, "email": true}
var updates bson.M
if err := json.Unmarshal(body, &updates); err != nil {
    ctx.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid JSON"})
    return
}
// Strip disallowed keys
for key := range updates {
    if !allowed[key] {
        delete(updates, key)
    }
}
_, err = collection.UpdateOne(ctx, bson.M{"_id": objID}, bson.M{"$set": updates})
// handle error

5. Apply tenant or organization scoping for multi-tenant designs

If data is organized by tenant, include the tenant ID in every filter, derived from the request context or subdomain, not from client input.

// Multi-tenant scoping
tenantID := ctx.Locals("tenantID").(string)
filter := bson.M{
    "tenant_id": tenantID,
    "slug":      params.Slug,
}
err = collection.FindOne(ctx, filter).Decode(&item)

middleBrick can verify that these patterns are followed by scanning endpoints without authentication and checking whether responses or side effects expose data beyond the intended scope. By aligning your implementation with these secure query and update strategies, you reduce the risk of IDOR, privilege escalation, and data exposure while maintaining compatibility with OpenAPI/Swagger analysis and continuous monitoring workflows offered by the Pro plan and integrations such as the GitHub Action and MCP Server.