Dictionary Attack in Chi with Cockroachdb

A dictionary attack in the context of a Chi web application using CockroachDB typically involves an attacker attempting to gain unauthorized access by systematically submitting a list of credential guesses against authentication endpoints. Chi is a lightweight HTTP router for Go, and when it exposes login or password-reset endpoints without adequate protections, those routes become targets for automated brute-force or credential-stuffing attempts.

When CockroachDB is used as the backend, the attack surface depends on how the application handles authentication queries. If Chi routes hit database-driven handlers that perform username lookups with non-constant-time operations, or if error messages differ between "user not found" and "incorrect password," an attacker can infer valid usernames. CockroachDB, being a distributed SQL database, does not inherently protect an application from logic flaws in how queries are constructed or how responses are returned. Therefore, a dictionary attack leverages insufficient rate limiting, weak account lockout policies, or verbose error handling in Chi handlers to iteratively test credentials against the CockroachDB-backed user store.

Consider an endpoint defined with Chi that queries CockroachDB by username. If the SQL query uses a simple string comparison and the application reveals whether a username exists based on query results, an attacker can harvest valid accounts. For example, a route like POST /login that runs SELECT id, password_hash FROM users WHERE username = $1 and responds differently when no row is found can be probed with a dictionary payload. The distributed nature of CockroachDB does not mitigate risks introduced by the API surface in Chi; without proper countermeasures such as uniform error responses, rate limiting, and secure password hashing, the system remains vulnerable.

In an unauthenticated scan, middleBrick would test the authentication and input validation checks around such endpoints, looking for indicators like inconsistent timing, information leakage in responses, or absence of rate controls. These checks align with broader OWASP API Top 10 concerns such as broken authentication and excessive data exposure. Proper remediation focuses on hardening the Chi routes and database interaction, rather than relying on the database layer alone to stop abuse.

To secure Chi routes that interact with CockroachDB, apply consistent error handling, parameterized queries, and defensive patterns that avoid leaking account existence. Below are concrete Go examples using the standard database/sql interface with a CockroachDB driver, integrated with Chi router handlers.

First, ensure your login handler uses a constant-time comparison approach and returns the same generic message regardless of user existence. Use context timeouts to prevent long-running queries and always employ prepared statements to avoid SQL injection.

// loginHandler handles POST /login securely
func loginHandler(db *sql.DB) http.HandlerFunc {
	return func(w http.ResponseWriter, r *http.Request) {
		ctx, cancel := context.WithTimeout(r.Context(), 5*time.Second)
		defer cancel()

		var username, password string
		if err := json.NewDecoder(r.Body).Decode(&struct {
			Username string `json:"username"`
			Password string `json:"password"`
		}{&Username: &username, &Password: &password}); err != nil {
			http.Error(w, `{"error": "invalid request"}`, http.StatusBadRequest)
			return
		}

		// Use parameterized query to prevent injection
		row := db.QueryRowContext(ctx, "SELECT id, password_hash, locked FROM users WHERE username = $1", username)
		var userID int
		var passwordHash string
		var locked bool
		err := row.Scan(&userID, &passwordHash, &locked)
		if err != nil {
			// Always respond the same way to avoid user enumeration
			w.Header().Set("Content-Type", "application/json")
			w.WriteHeader(http.StatusUnauthorized)
			json.NewEncoder(w).Encode(map[string]string{"error": "Invalid credentials"})
			return
		}
		if locked {
			w.Header().Set("Content-Type", "application/json")
			w.WriteHeader(http.StatusForbidden)
			json.NewEncoder(w).Encode(map[string]string{"error": "Account locked"})
			return
		}

		// Use a constant-time comparison function for password verification
		if !constantTimeCompare(hashPassword(password, passwordHash)) {
			w.Header().Set("Content-Type", "application/json")
			w.WriteHeader(http.StatusUnauthorized)
			json.NewEncoder(w).Encode(map[string]string{"error": "Invalid credentials"})
			return
		}

		// Issue session token, etc.
		token := issueSessionToken(userID)
		w.Header().Set("Content-Type", "application/json")
		json.NewEncoder(w).Encode(map[string]string{"token": token})
	}
}

// constantTimeCompare prevents timing attacks
func constantTimeCompare(a, b string) bool {
	if subtle.ConstantTimeEq(int32(len(a)), int32(len(b))) != 1 {
		return false
	}
	return subtle.ConstantTimeCompare([]byte(a), []byte(b)) == 1
}

// hashPassword derives a hash for comparison (use a proper KDF in production)
func hashPassword(password, hash string) string {
	// Placeholder: use bcrypt or argon2
	return hash
}

Additionally, enforce rate limiting at the Chi middleware level to mitigate dictionary attacks. You can use a sliding window store to track failed attempts per IP or per username key without introducing stateful agents.

// rateLimitMiddleware limits login attempts
func rateLimitMiddleware(next http.Handler) http.Handler {
	// Use a concurrent map or external store in production
	attempts := make(map[string]int)
	var mu sync.Mutex

	return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
		ip := r.RemoteAddr
		mu.Lock()
		defer mu.Unlock()
		attempts[ip]++
		if attempts[ip] > 10 {
			http.Error(w, `{"error": "too many requests"}`, http.StatusTooManyRequests)
			return
		}
		next.ServeHTTP(w, r)
	})
}

For production, integrate with a distributed store to share rate limits across nodes, and ensure CockroachDB user queries use indexes on username to maintain predictable performance. middleBrick’s scans can verify that such protections are in place by checking for rate limiting controls and uniform error handling in the API surface.