Unicode Normalization in Fiber with Cockroachdb

Unicode normalization inconsistencies between HTTP request handling in Fiber and string comparison in Cockroachdb can lead to authentication bypass or IDOR-like behavior. When a client sends an HTTP request, characters such as é can be represented in multiple Unicode forms: composed (U+00E9) or decomposed (U+0065 U+0301). If Fiber does not normalize input before using it to construct database queries, and Cockroachdb stores or compares strings in a different normalization form, two seemingly identical identifiers may not match, causing the application to treat them as distinct.

For example, a user registration might store the username using the composed form, while a login request provides the decomposed form. If the lookup in Cockroachdb relies on a direct string equality check without normalization, the query may fail to match the stored record. An attacker could exploit this by supplying a carefully crafted Unicode variant to access another user’s account or data, effectively bypassing identity checks that appear correct at the application layer. This class of issue maps to OWASP API Top 10 authentication and authorization flaws and can be surfaced by middleBrick’s BOLA/IDOR and Authentication checks.

In a distributed SQL setup like Cockroachdb, normalization mismatches can also affect index usage and query results across nodes if collation or comparison rules are not consistently applied. The database may perform comparisons based on byte-level ordering rather than linguistic equivalence, which can differ from what Fiber’s runtime expects. middleBrick’s OpenAPI/Swagger analysis, with full $ref resolution, can detect endpoints where user-controlled string parameters flow into database queries without canonicalization, helping to highlight where normalization should be enforced. The scanner runs in 5–15 seconds, testing the unauthenticated attack surface, and its LLM/AI Security checks additionally look for prompt injection risks that could manipulate backend logic around user input handling.

Concrete risks include bypassing rate limiting or property-level authorization when a normalized identifier is expected but a non-normalized value is provided. Data Exposure findings may also appear if normalized and non-normalized forms map to different permissions, allowing one user to infer the existence of another’s data. middleBrick’s per-category breakdowns provide prioritized findings with severity and remediation guidance, enabling developers to address these issues before deployment. Using the CLI tool (middlebrick scan <url>) or the GitHub Action to add API security checks to CI/CD pipelines can help enforce normalization as part of the build gate.

To mitigate Unicode normalization issues, normalize all user-controlled strings to a standard form, such as NFC, before using them in Cockroachdb queries. In Fiber, implement a middleware or handler that applies normalization as early as possible. Below is a concrete example using Go with the Fiber framework and the Cockroachdb Go driver, demonstrating how to normalize input and safely use parameterized queries.

// main.go
package main

import (
    "database/sql"
    "golang.org/x/text/unicode/norm"
    "github.com/gofiber/fiber/v2"
    _ "github.com/lib/pq"
)

// normalize returns the NFC form of the input string.
func normalize(s string) string {
    return norm.NFC.String(s)
}

func main() {
    app := fiber.New()
    db, err := sql.Open("postgres", "postgresql://user:password@host:26257/dbname?sslmode=disable")
    if err != nil {
        panic(err)
    }
    defer db.Close()

    // Example: lookup user by normalized username
    app.Get("/user/:username", func(c *fiber.Ctx) error {
        raw := c.Params("username")
        key := normalize(raw)

        var email string
        // Use parameterized queries to avoid injection and ensure consistent comparison.
        row := db.QueryRow("SELECT email FROM users WHERE username = $1", key)
        if err := row.Scan(&email); err != nil {
            if err == sql.ErrNoRows {
                return c.Status(404).SendString("user not found")
            }
            return c.Status(500).SendString("server error")
        }
        return c.JSON(fiber.Map{"username": key, "email": email})
    })

    // Example: ensure uniqueness constraint with normalization on write
    app.Post("/user", func(c *fiber.Ctx) error {
        type Payload struct {
            Username string `json:"username"`
            Email    string `json:"email"`
        }
        var p Payload
        if err := c.BodyParser(&p); err != nil {
            return c.Status(400).SendString("invalid payload")
        }
        key := normalize(p.Username)

        _, err := db.Exec("INSERT INTO users (username, email) VALUES ($1, $2)", key, p.Email)
        if err != nil {
            // Handle unique violation if a duplicate normalized username is inserted.
            return c.Status(409).SendString("username already exists")
        }
        return c.JSON(fiber.Map{"username": key})
    })

    app.Listen(":3000")
}

On the Cockroachdb side, ensure the column used for lookups has a consistent collation or that comparisons are performed in a normalized context. If you store normalized values, create indexes on the normalized column to maintain performance. The following SQL illustrates a table and an index aligned with the Go code above:

-- SQL schema in Cockroachdb
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    username STRING UNIQUE NOT NULL,
    email STRING NOT NULL
);

-- Index on normalized username if you normalize at write time.
-- This is optional if username is already the primary key or has a unique constraint.
CREATE INDEX idx_users_username ON users (username);

For additional safety, you can normalize at query time within Cockroachdb using built-in functions if your collation supports it, but it is generally more efficient to normalize once at the application layer and enforce uniqueness constraints. middleBrick’s Pro plan includes continuous monitoring and can alert you if endpoints exhibit inconsistent handling of Unicode across requests. The MCP Server allows you to run scans directly from your AI coding assistant, helping to catch normalization issues early in development.