Dictionary Attack in Buffalo with Cockroachdb

A dictionary attack in the Buffalo framework when backed by Cockroachdb typically arises from weak authentication endpoints combined with predictable user identifiers. Buffalo generates forms and handlers that, if left customized only at the UI level, may still accept common usernames or emails and perform SQL queries that are not sufficiently protected against brute-force or credential-stuffing patterns. Cockroachdb, while strongly consistent and resilient to node failures, does not inherently prevent high-rate login attempts against a single user or a set of known accounts. If the application does not enforce rate limiting or account lockout, an attacker can submit many password guesses per second, leveraging Cockroachdb’s low-latency local reads to validate credentials quickly. The risk is higher when session tokens or password reset tokens are predictable, or when error messages differ between "user not found" and "incorrect password", allowing username enumeration. Because Buffalo encourages rapid prototyping, developers might skip adding throttling or multi-factor authentication early in development, leaving the authentication surface exposed. The combination of Buffalo’s fast iteration and Cockroachdb’s performance can therefore make dictionary attacks more practical if authentication controls are not explicitly designed.

OpenAPI/Swagger analysis can expose authentication weaknesses when endpoints lack security schemes or use weak security schemes. middleBrick scans such endpoints during its 12 parallel checks, including Authentication, BOLA/IDOR, and Rate Limiting, and maps findings to frameworks like OWASP API Top 10. If an unauthenticated endpoint accepts login-like inputs without strict input validation or rate controls, middleBrick flags it with remediation guidance. This is valuable because it shows how implementation choices in Buffalo, when paired with Cockroachdb, can unintentionally ease automated guessing. The scanner does not fix the code, but provides prioritized findings and guidance, such as adding exponential backoff, account lockout, or CAPTCHA, and ensuring consistent error responses to prevent user enumeration.

To harden authentication in Buffalo against dictionary attacks when using Cockroachdb, apply rate limiting and account lockout at the handler level, and ensure all database interactions use parameterized statements to prevent injection. Below is a concrete example of a login handler that uses Cockroachdb via the database/sql interface, with per-username attempt tracking stored in a separate table. This approach avoids relying on in-memory limits and survives server restarts.

-- Migration to store login attempt counts per user
CREATE TABLE IF NOT EXISTS login_attempts (
    user_id UUID PRIMARY KEY REFERENCES users(id) ON DELETE CASCADE,
    attempts INT NOT NULL DEFAULT 0,
    last_attempt TIMESTAMPTZ NOT NULL DEFAULT now()
);

In your Buffalo controller, implement throttling logic before checking credentials:

func (v AuthController) Login(c buffalo.Context) error {
    var req struct {
        Email    string `json:"email" validate:"required,email"`
        Password string `json:"password" validate:"required,min=8"`
    }
    if err := c.Bind(&req); err != nil {
        return c.Render(400, r.JSON(map[string]string{"error": "invalid request"}))
    }
    // Input validation and normalization
    email := strings.ToLower(strings.TrimSpace(req.Email))

    tx := c.Value("tx").(*gorp.Transaction)
    var attemptCount int
    var lastAttempt time.Time
    err := tx.SelectOne(&attemptCount, "SELECT attempts, last_attempt FROM login_attempts WHERE user_id = (SELECT id FROM users WHERE email = $1)", email)
    if err != nil {
        // If no row exists, treat as zero attempts
        attemptCount = 0
        lastAttempt = time.Time{}
    }
    // Simple rate limit: max 5 attempts per 15 minutes
    if attemptCount >= 5 && time.Since(lastAttempt) < 15*time.Minute {
        return c.Render(429, r.JSON(map[string]string{"error": "too many attempts, try later"}))
    }

    var userID uuid.UUID
    var hashedPass string
    err = tx.Get(&userID, "SELECT id, password_hash FROM users WHERE email = $1", email)
    if err != nil {
        // Use a constant-time dummy check to avoid timing leaks
        dummyHash := bcrypt.GenerateFromPassword([]byte("dummy"), bcrypt.DefaultCost)
        bcrypt.CompareHashAndPassword(dummyHash, []byte(req.Password))
        // Still record an attempt to prevent timing-based enumeration
        v.recordLoginAttempt(tx, email, false)
        return c.Render(401, r.JSON(map[string]string{"error": "invalid credentials"}))
    }
    if err := bcrypt.CompareHashAndPassword([]byte(hashedPass), []byte(req.Password)); err != nil {
        v.recordLoginAttempt(tx, email, true)
        return c.Render(401, r.JSON(map[string]string{"error": "invalid credentials"}))
    }
    // Reset attempts on successful login
    v.resetLoginAttempts(tx, email)
    // Issue session token...
    return c.Render(200, r.JSON(map[string]string{"ok": "true"}))
}

// Helper methods on the controller
func (v AuthController) recordLoginAttempt(tx *gorp.Transaction, email string, wasUser bool) {
    if wasUser {
        tx.Exec("INSERT INTO login_attempts (user_id, attempts, last_attempt) VALUES ((SELECT id FROM users WHERE email = $1), COALESCE((SELECT attempts FROM login_attempts WHERE user_id = (SELECT id FROM users WHERE email = $1)), 0) + 1, now()) ON CONFLICT (user_id) DO UPDATE SET attempts = login_attempts.attempts + 1, last_attempt = now()", email)
    } else {
        // Record attempt without revealing user existence
        dummyID := uuid.Nil
        tx.Exec("INSERT INTO login_attempts (user_id, attempts, last_attempt) VALUES ($1, 1, now()) ON CONFLICT (user_id) DO NOTHING", dummyID)
    }
}

func (v AuthController) resetLoginAttempts(tx *gorp.Transaction, email string) {
    tx.Exec("DELETE FROM login_attempts WHERE user_id = (SELECT id FROM users WHERE email = $1)", email)
}

Additionally, configure your application to use secure cookies, enable HTTPS, and consider adding CAPTCHA after repeated failures. middleBrick’s CLI can be used in your pipeline to verify that authentication endpoints include proper rate controls and consistent error handling, helping you catch misconfigurations before deployment.