Insecure Design in Echo Go with Mongodb

Insecure design in an Echo Go service that uses MongoDB often arises from how application-level decisions expose the database and bypass layered defenses. A common pattern is constructing MongoDB queries by concatenating user input directly into filter documents, which couples business logic with data access and makes injection and data exposure likely. For example, building a filter with raw query parameters without validation or type checking can allow an attacker to manipulate query structure, bypass intended access controls, and retrieve or modify data that should be isolated per tenant or user.

When authorization checks are implemented at the HTTP handler layer but not enforced within the data access layer, the design creates a BOLA/IDOR vector: a direct path to another user’s data through predictable identifiers. Because MongoDB supports rich query expressions and partial match operators, an attacker can supply crafted values (e.g., { "$ne": null }) to enumerate records or force broader result sets. This becomes more dangerous when combined with missing or misconfigured field-level permissions, allowing excessive data exposure through a single endpoint.

The combination of Echo Go’s flexible routing and MongoDB’s flexible schema can also weaken input validation and property authorization. If numeric IDs are parsed as strings and used without type assertions, or if ObjectId validation is skipped, malformed values can lead to server-side errors or information leakage. SSRF risks increase when user-controlled strings are used to construct MongoDB connection or aggregation stages that include $lookup or $graphLookup without strict allowlists. In designs where the API surface maps closely to MongoDB collections without an abstraction layer, changes in schema or indexing can unintentionally change access behavior, making it difficult to maintain consistent authorization across operations.

Compliance mappings highlight the impact: insecure MongoDB usage in Echo Go can violate OWASP API Top 10 (2023) A01:2023 Broken Object Level Authorization and A03:2023 Injection, and it can affect PCI-DSS requirements for data access control and SOC2 controls around logical access. These issues are detectable in scans because they manifest as missing validation, missing ownership checks, and overly permissive query patterns that allow unauthenticated or underauthorized data access.

Remediation focuses on strict input validation, canonical query construction, and consistent authorization checks in the data access layer. Use strong typing for IDs, validate and convert to MongoDB ObjectId before querying, and avoid building filters from raw query parameters. Always enforce ownership checks in the query itself rather than relying on application-level filtering.

Example: Safe query construction with ObjectId validation

import (
    "github.com/labstack/echo/v4"
    "go.mongodb.org/mongo-driver/bson"
    "go.mongodb.org/mongo-driver/bson/primitive"
    "net/http"
    "strconv"
)

func getUserProfile(c echo.Context) error {
    userID := c.Param("id")
    uid, err := primitive.ObjectIDFromHex(userID)
    if err != nil {
        return echo.NewHTTPError(http.StatusBadRequest, "invalid user id")
    }
    // Enforce ownership: include the requesting user’s ID in the filter
    filter := bson.D{
        {Key: "_id", Value: uid},
        {Key: "tenant_id", Value: c.Get("tenantID")},
    }
    var profile UserProfile
    err = db.Collection("profiles").FindOne(c.Request().Context(), filter).Decode(&profile)
    if err != nil {
        return echo.NewHTTPError(http.StatusNotFound, "profile not found")
    }
    return c.JSON(http.StatusOK, profile)
}

Example: Parameterized aggregation with allowlisted lookup

import (
    "github.com/labstack/echo/v4"
    "go.mongodb.org/mongo-driver/bson"
    "net/http"
)

func searchItems(c echo.Context) error {
    category := c.QueryParam("category")
    // Allowlist validation to prevent injection via $lookup stages
    allowlisted := map[string]bool{"electronics": true, "books": true, "clothing": true}
    if !allowlisted[category] {
        return echo.NewHTTPError(http.StatusBadRequest, "invalid category")
    }
    pipeline := mongo.Pipeline{
        {{"$match", bson.D{{Key: "category", Value: category}}}}, // safe, allowlisted
        {{"$lookup", bson.D{
            {Key: "from", Value: "inventory"},
            {Key: "localField", Value: "sku"},
            {Key: "foreignField", Value: "sku"},
            {Key: "as", Value: "stock"},
        }}},
    }
    cursor, err := db.Collection("products").Aggregate(c.Request().Context(), pipeline)
    if err != nil {
        return echo.NewHTTPError(http.StatusInternalServerError, "aggregation failed")
    }
    var results []bson.M
    if err = cursor.All(c.Request().Context(), &results); err != nil {
        return echo.NewHTTPError(http.StatusInternalServerError, "failed to decode results")
    }
    return c.JSON(http.StatusOK, results)
}

General design practices

• Use parameterized queries and avoid string concatenation for filters or aggregation stages.
• Validate and convert IDs to MongoDB ObjectId; reject malformed values early.
• Embed tenant or user ownership in every query filter to enforce BOLA protections at the database level.
• Apply allowlists for any user-influenced values used in $lookup, $graphLookup, or dynamic stage construction.
• Log query patterns and error types for anomaly detection, but do not expose raw user input in error messages.