Password Spraying in Chi with Cockroachdb

Password spraying is an authentication attack that attempts a small number of common passwords against many accounts. When an API built with Chi uses CockroachDB as its backend, specific implementation patterns can unintentionally enable or amplify this behavior. The risk typically arises when endpoints authenticate users by username or email and perform a lookup against CockroachDB without first establishing that the account exists, then applying a constant-time password comparison. If the existence check is not decoupled from the password verification step, an attacker can infer valid usernames by observing whether the endpoint returns an account-specific error versus a generic authentication failure.

In a Chi application, routes often map directly to handler functions that query CockroachDB using SQL rows or prepared statements. For example, a login handler might first SELECT a user row by email. If that SELECT returns no rows, the handler may immediately return a 401 with a message like “invalid credentials” before ever invoking the password comparison. This distinct response path reveals account existence. An attacker conducting a password spray can iterate through known usernames or emails and observe which identifiers produce a different error, narrowing the list of valid accounts even though the password checks themselves remain rate-limited or generic.

CockroachDB’s distributed SQL nature does not inherently cause this issue, but the way queries are structured in Chi handlers does. If the existence check and password verification are separate operations and not consistently protected, timing differences or response code variations can leak information. For instance, a query like SELECT id, password_hash FROM users WHERE email = $1 will return no rows for unknown emails, while a row return indicates the account exists. Even if the subsequent bcrypt comparison runs for a constant duration, the absence of a row can cause an early exit that changes response timing or content in detectable ways.

Moreover, if the Chi application logs failed attempts per username and those logs are accessible or aggregated, attackers can correlate spray patterns across IPs. CockroachDB audit logs or application-level instrumentation may record each SELECT and its result, providing data that can be mined to refine the spray. Because password spraying relies on low-and-slow attempts to evade rate-based protections, any distinguishing signal—such as a unique error code, HTTP status, or response header—amplifies the attack’s efficiency.

To mitigate this in a Chi service, ensure that the authentication flow always performs a constant-time operation regardless of account existence, and avoid branching responses on SELECT presence. Use a single, uniform path that runs a password comparison against a synthetic hash when the row is absent, and enforce global rate limits and backoff policies at the edge or middleware layer. middleBrick can support this posture by scanning your Chi endpoints for authentication anomalies, such as inconsistent timing or unauthenticated LLM endpoints, and by checking whether OpenAPI specs expose auth-sensitive paths without proper protections.

Remediation focuses on making the login path indistinguishable whether the account exists or not. In Chi, this means structuring handlers so that SELECT and password verification follow the same control flow and timing characteristics. Below is a secure pattern using prepared statements and constant-time comparison.

// Chi login handler with CockroachDB-safe authentication
package main

import (
    "context"
    "crypto/subtle"
    "net/http"
    "time"

    "github.com/go-chi/chi/v5"
    "github.com/jackc/pgx/v5/pgxpool"
    "golang.org/x/crypto/bcrypt"
)

func loginHandler(pool *pgxpool.Pool) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        ctx := r.Context()
        var email, password string
        // parse JSON body or form values
        // ...

        // Always fetch a row; if missing, use a synthetic hash
        row := pool.QueryRow(ctx, "SELECT id, password_hash FROM users WHERE email = $1", email)
        var id int
        var storedHash string
        err := row.Scan(&id, &storedHash)

        // Use a default hash that bcrypt can verify in roughly the same time
        const defaultHash = "$2a$10$012345678901234567890u"
        if err != nil {
            storedHash = defaultHash
        }

        // Constant-time comparison to prevent timing leaks
        if bcrypt.CompareHashAndPassword([]byte(storedHash), []byte(password)) != nil {
            http.Error(w, `{"error": "invalid credentials"}`, http.StatusUnauthorized)
            return
        }

        // Proceed with session creation
        w.WriteHeader(http.StatusOK)
    }
}

This handler ensures that a row is always fetched; if the SELECT returns no rows, it substitutes a default bcrypt hash that still incurs a comparable computational cost. The use of bcrypt.CompareHashAndPassword provides constant-time comparison, and the response is uniform for both missing accounts and incorrect passwords. In production, rotate the default hash periodically to avoid predictability.

Additionally, apply middleware-level rate limiting and consider enabling middleBrick’s CLI or dashboard to monitor scan results against your Chi endpoints. The Pro plan can enforce continuous monitoring and integrate with GitHub Actions to gate CI/CD if a related regression appears in OpenAPI specs. For teams using AI-assisted development, the MCP Server allows scanning API designs directly from the editor, catching authentication flow issues before deployment.