Log Injection in Echo Go with Firestore

Log injection occurs when untrusted input is written directly into log entries without sanitization, enabling an attacker to forge log lines, hide activity, or trigger misinterpretation by log aggregation tools. When you build an API with the Echo framework in Go and store or reference data in Firestore, several characteristics of this combination can amplify the risk.

Echo provides convenient context logging helpers such as echo.Logger and middleware that attach request IDs, user identifiers, and timestamps to structured logs. If a handler directly interpolates user-controlled values—such as a document ID, query parameter, or request body field—into these logs before validating or escaping them, the resulting log line can contain newline characters or structured delimiters that break the expected log format.

Firestore operations in Go typically involve document paths, map-based data structures, and custom structs that you marshal for logging. Consider a handler that logs a Firestore document read: if the document ID comes from the client and is appended verbatim to a log message, an attacker can supply IDs like abc\n{"level":"ERROR"} transaction_failed. When the log line is ingested by a system that parses structured logs or uses newline-based splitting, the injected payload can create a fictitious log entry that appears authoritative.

In distributed systems that correlate requests using IDs stored in Firestore, log injection can obscure the true origin of a request. For example, an attacker might inject a fabricated trace ID that matches a benign-looking Firestore document path, causing security analysts to misattribute events. Because Firestore document paths and keys often appear verbatim in logs (for debugging or auditing), unsanitized output can distort metrics and trigger false alerts in monitoring dashboards.

Additionally, Echo’s error-handling patterns can inadvertently amplify injection. When handlers return HTTP errors with details sourced from Firestore (e.g., document not found), logging these details without sanitization may embed carriage returns or JSON-like fragments into the log stream. This not only complicates log parsing but also aids in crafting multi-line injection attacks that evade simple regex-based defenses.

The combination of Echo’s flexible logging utilities, Firestore’s document-centric data model, and the presence of user-controlled identifiers creates a scenario where log injection is plausible and impactful. Detecting these patterns requires review of log-generation code paths that reference Firestore metadata and ensuring that any data destined for logs is normalized and escaped.

Remediation focuses on strict input validation, structured logging with explicit field separation, and avoiding direct interpolation of untrusted data into log messages. Below are concrete, realistic examples for Echo handlers that interact with Firestore.

1. Validate and sanitize document identifiers

Ensure document IDs conform to an allowlist pattern before using them in Firestore operations or logs. Reject paths that contain newline characters or control characters.

package main

import (
	"regexp"
	"strings"

	"github.com/labstack/echo/v4"
	"cloud.google.com/go/firestore"
)

var docIDRe = regexp.MustCompile(`^[a-zA-Z0-9][a-zA-Z0-9\-._~]{0,126}$`)

func isValidDocID(id string) bool {
	return docIDRe.MatchString(id) && !strings.ContainsAny(id, "\n\r")
}

2. Use structured logging with explicit field assignment

Instead of string concatenation, log key-value pairs so log consumers can reliably parse fields. This prevents newline injection from corrupting record boundaries.

package main

import (
	"go.uber.org/zap"
	"github.com/labstack/echo/v4"
)

func setupLogger() *zap.Logger {
	cfg := zap.NewProductionConfig()
	l, _ := cfg.Build()
	return l
}

func getUserDoc(c echo.Context, client *firestore.Client) error {
	l := setupLogger()
	defer l.Sync()

	docID := c.Param("id")
	if !isValidDocID(docID) {
		l.Warn("invalid document ID",
			zap.String("doc_id", docID),
			zap.String("path", c.Request().RequestURI),
		)
		return echo.NewHTTPError(400, "invalid document identifier")
	}

	dsnap, err := client.Collection("items").Doc(docID).Get(c.Request().Context())
	if err != nil {
		l.Error("failed to fetch document",
			zap.String("doc_id", docID),
			zap.Error(err),
		)
		return echo.NewHTTPError(500, "unable to retrieve document")
	}

	l.Info("document retrieved",
		zap.String("doc_id", docID),
		zap.String("collection", "items"),
		zap.Bool("exists", dsnap.Exists()),
	)

	return c.JSON(200, dsnap.Data())
}

3. Avoid logging raw Firestore error messages that may contain user input

Firestore errors can include document paths or field values supplied by the client. Sanitize these before logging.

func safeFirestoreError(err error, docID string) {
	l := setupLogger()
	l.Error("firestore operation failed",
		zap.String("doc_id", docID),
		zap.String("error_code", err.Error()), // do not include raw user input
	)
}

4. Normalize output when referencing Firestore data in logs

If you must include data from Firestore documents in logs, explicitly select safe fields and escape newlines.

type SafeRecord struct {
	ID   string `json:"id"`
	Name string `json:"name"`
}

func logFromDoc(snap *firestore.DocumentSnapshot) SafeRecord {
	var rec SafeRecord
	_ = snap.DataTo(&rec)
	// Ensure no newlines in logged fields
	rec.Name = strings.ReplaceAll(rec.Name, "\n", " ")
	return rec
}

By combining strict validation, structured logging, and careful sanitization, you reduce the attack surface for log injection while maintaining useful audit trails for Firestore interactions in Echo-based services.