Insufficient Logging in Fiber with Mongodb

Insufficient Logging in a Fiber application that uses Mongodb as a data store can leave critical security events unrecorded, reducing the ability to detect, investigate, or respond to incidents. When requests interact with the database, a lack of structured logs for both application-level actions and database-level operations means suspicious patterns—such as repeated authentication failures, unexpected query filters, or unauthorized data exports—are not reliably captured.

In a typical Fiber + Mongodb setup, developers may log only high-level HTTP status codes or simple request paths while omitting details such as which database operation was attempted, which query filters were applied, or which user identity (or unauthenticated context) initiated the call. For example, if an endpoint builds a Mongodb query using user-supplied parameters without logging the resolved filter or projection, an attacker can probe for IDOR or BOLA behaviors without leaving an auditable trace. This lack of visibility is especially risky when combined with permissive error handling that swallows database exceptions, because the application may return generic errors while the underlying query or connection issue remains invisible to defenders.

Logging gaps also extend to authentication and authorization events. Without explicit logs for token validation outcomes, role assignments, or permission checks tied to Mongodb-stored user data, it is difficult to confirm whether access controls are being enforced as intended. Meanwhile, the database itself may provide its own operation metrics, but if those are not correlated with request identifiers or user contexts in the application logs, investigations require stitching together disjointed sources. This increases mean time to detection and can allow an attacker to iterate through endpoints, such as testing unauthenticated LLM endpoints or injecting malicious payloads, without triggering an alarm.

middleBrick scans highlight such logging insufficiencies by testing the unauthenticated attack surface of a Fiber + Mongodb service and observing whether essential events are recorded. For instance, it can attempt to trigger error conditions or traverse IDOR-prone routes and then check whether the resulting database interactions, input validation failures, or authorization denials appear in logs with sufficient detail. The scanner also evaluates whether sensitive data, such as API keys or PII, is inadvertently exposed through verbose error messages that bypass intended logging safeguards. These checks emphasize the need for consistent, context-rich logging across both the application layer and the database layer to support timely detection and remediation.

When integrating with compliance frameworks, insufficient logging in this stack maps directly to gaps in auditability required by OWASP API Top 10 (e.g., A04:2023 – Insecure Design) and can affect controls in SOC2 and GDPR. A robust approach combines structured application logs with correlated Mongodb operation details, including query shapes, filter patterns, and outcome statuses, while ensuring logs themselves do not become a data exposure vector by capturing secrets or tokens.

To address insufficient logging in Fiber applications using Mongodb, implement structured, context-aware logging at both the HTTP and database layers. This ensures each request and database interaction is recorded with sufficient detail for security monitoring, while avoiding the inclusion of sensitive information.

1. Structured HTTP and operation logging

Log key attributes for every incoming request, including method, path, correlation ID, user identity (or unauthenticated marker), and outcome. Use structured logging (e.g., JSON) to simplify ingestion by SIEM or log analysis tools.

// main.go
package main

import (
	"context"
	"log/slog"
	"net/http"
	"github.com/gofiber/fiber/v2"
	"go.mongodb.org/mongo-driver/mongo"
)

var logger = slog.New(slog.NewJSONHandler(os.Stderr, &slog.HandlerOptions{Level: slog.LevelInfo}))

func logRequest(c *fiber.Ctx, next fiber.Handler) error {
	correlationID := c.Get("X-Request-ID", "unknown")
	logger.Info("http_request_start",
		"method", c.Method(),
		"path", c.Path(),
		"correlation_id", correlationID,
		"user", c.Locals("user")), // set by auth middleware
	err := next(c)
	status := c.Response().StatusCode()
	logger.Info("http_request_done",
		"method", c.Method(),
		"path", c.Path(),
		"correlation_id", correlationID,
		"status", status,
		"error", err != nil),
	return err
}

2. Mongodb operation logging with correlation

Wrap database calls to log the operation type, collection name, filter keys (without values), projection, and outcome. Use the same correlation ID from the HTTP layer to link logs across systems.

// db/collection.go
package db

import (
	"context"
	"log/slog"
	"go.mongodb.org/mongo-driver/bson"
	"go.mongodb.org/mongo-driver/mongo"
)

var logger = slog.New(slog.NewJSONHandler(os.Stderr, &slog.HandlerOptions{Level: slog.LevelInfo}))

func FindUserByEmail(ctx context.Context, client *mongo.Client, database, collection, email string, correlationID string) ([]bson.M, error) {
	coll := client.Database(database).Collection(collection)
	filter := bson.M{"email": email}
	// Log the filter keys but avoid logging raw sensitive values where possible
	logger.Info("mongodb_query",
		"correlation_id", correlationID,
		"database", database,
		"collection", collection,
		"operation", "find",
		"filter_keys", []string{"email"},
		"projection", nil)

	cursor, err := coll.Find(ctx, filter)
	if err != nil {
		logger.Warn("mongodb_query_failed",
			"correlation_id", correlationID,
			"error", err.Error())
		return nil, err
	}
	defer cursor.Close(ctx)
	var results []bson.M
	if err = cursor.All(ctx, &results); err != nil {
		logger.Warn("mongodb_decode_failed",
			"correlation_id", correlationID,
			"error", err.Error())
		return nil, err
	}
	logger.Info("mongodb_query_success",
		"correlation_id", correlationID,
		"operation", "find",
		"matched", len(results))
	return results, nil
}

3. Authentication and authorization event logging

Log token validation outcomes and permission checks, tying them to the user identity and requested resource to support access control audits.

// middleware/auth.go
package middleware

import (
	"log/slog"
	"net/http"
	"github.com/gofiber/fiber/v2"
)

var logger = slog.New(slog.NewJSONHandler(os.Stderr, &slog.HandlerOptions{Level: slog.LevelInfo}))

func AuthMiddleware(c *fiber.Ctx) error {
	user, claims, err := validateToken(c)
	correlationID := c.Get("X-Request-ID", "unknown")
	if err != nil {
		logger.Warn("auth_failure",
			"correlation_id", correlationID,
			"error", err.Error())
		return c.SendStatus(http.StatusUnauthorized)
	}
	logger.Info("auth_success",
		"correlation_id", correlationID,
		"user_id", claims["sub"])
	c.Locals("user", user)
	return c.Next()
}

func RequireRole(role string) fiber.Handler {
	return func(c *fiber.Ctx) error {
		user := c.Locals("user")
		correlationID := c.Get("X-Request-ID", "unknown")
		if !hasRole(user, role) {
			logger.Warn("authz_denied",
				"correlation_id", correlationID,
				"user_id", userID,
				"required_role", role)
			return c.SendStatus(http.StatusForbidden)
		}
		logger.Info("authz_granted",
			"correlation_id", correlationID,
			"user_id", userID,
			"required_role", role)
		return c.Next()
	}
}

4. Avoid logging sensitive data

Ensure logs do not capture full tokens, raw passwords, or entire documents. Redact or hash where necessary, and control log levels in production to reduce exposure risk.

By combining HTTP request logging with granular Mongodb operation records, the Fiber + Mongodb stack gains improved auditability, faster incident investigation, and clearer mapping to compliance requirements such as OWASP API Top 10 and SOC2 audit trails.