Dictionary Attack in Echo Go with Hmac Signatures

A dictionary attack against an Echo Go service that uses HMAC signatures can be successful when the endpoint accepts high‑entropy inputs (such as passwords or API keys) as part of the signed payload and does not enforce rate limiting or request throttling. In such a setup, the client typically computes an HMAC over a subset of the request data (for example, a timestamp, a user identifier, and a resource path) using a shared secret, then sends the signature in a header. If the server processes each candidate guess sequentially and returns distinct HTTP status codes or timing differences for valid versus invalid signatures, an attacker can learn information about signature validity without needing to know the secret.

In Echo Go, a common pattern is to read a secret from configuration, compute HMAC‑SHA256 over a canonical string, and compare it to the client‑supplied signature using a constant‑time function. However, if the comparison is performed inside the request handling flow without additional controls, an attacker can send many requests per second with slightly altered inputs (e.g., iterating over a list of likely passwords or API keys) and observe whether the server begins to enforce other protections (such as account lockout) only after numerous failures. The risk is compounded when the signed data includes a user‑controlled identifier (like a username or API key) that does not change between attempts, because the attacker can reuse a known valid signature structure while varying only the guessed secret or input.

Moreover, if the Echo Go service does not bind the signature verification to a per‑user or per‑client rate limit, a single attacker can generate thousands of HMAC verification attempts within a short window. Because HMAC itself is deterministic, the server’s responses may leak validity through timing side channels or differing application logic paths (for example, returning 401 vs 403, or performing extra checks only when the signature matches). An attacker who can measure response times or infer status codes can iteratively refine guesses, effectively mounting a dictionary attack against the shared secret or the input space covered by the signed payload.

OpenAPI/Swagger analysis helps identify such risks by showing which endpoints accept high‑entropy inputs and whether the spec documents security schemes based on HMAC. Runtime findings from a scan can highlight endpoints where the server does not enforce rate limiting or where response behaviors differ between valid and invalid signatures. This aligns with checks such as Authentication, Rate Limiting, and Input Validation, which are run in parallel by middleBrick to surface the conditions that make dictionary attacks feasible in an Echo Go implementation that relies on HMAC signatures.

Remediation focuses on reducing the attacker’s ability to test many signatures and ensuring that signature verification does not leak validity through behavior or timing. The following patterns are specific to Echo Go and demonstrate how to implement HMAC verification safely.

1. Use constant‑time comparison and avoid early branching on signature validity.

package main

import (
    "crypto/hmac"
    "crypto/sha256"
    "net/http"
    "time"

    "github.com/labstack/echo/v4"
)

func verifySignature(secret, data, receivedSig string) bool {
    mac := hmac.New(sha256.New, []byte(secret))
    mac.Write([]byte(data))
    expected := mac.Sum(nil)
    // Use hmac.Equal for constant‑time comparison
    return hmac.Equal(expected, []byte(receivedSig))
}

func signedHandler(secret string) echo.HandlerFunc {
    return func(c echo.Context) error {
        receivedSig := c.Request().Header.Get("X-Signature")
        timestamp := c.Request().Header.Get("X-Timestamp")
        // Include a per‑request nonce or a fixed server‑side payload to bind the signature
        payload := timestamp + ":/v1/resource"
        if !verifySignature(secret, payload, receivedSig) {
            // Always return the same generic error and status
            return c.JSON(http.StatusUnauthorized, map[string]string{"error": "unauthorized"})
        }
        // Proceed with request handling
        return c.JSON(http.StatusOK, map[string]string{"status": "ok"})
    }
}

2. Enforce strict rate limiting per client identifier before performing HMAC verification.

package main

import (
    "github.com/labstack/echo/v4"
    "github.com/labstack/echo/v4/middleware"
    "net/http"
)

func main() {
    e := echo.New()
    // Rate limit: 10 requests per minute per IP
    e.Use(middleware.RateLimiter(middleware.DefaultRateLimiterConfig))
    // Optional: use a custom rate limiter keyed by API key extracted from headers
    secret := "my-secure-shared-secret"
    e.POST("/resource", func(c echo.Context) error {
        return signedHandler(secret)(c)
    })
    e.Logger.Fatal(e.Start(":8080"))
}

3. Bind signature verification to a per‑user or per‑API‑key context to prevent a single shared secret from being exercised broadly without tracking. If you must use a shared secret, rotate it periodically and avoid including user‑identifiers that an attacker can enumerate in the signed payload without additional protections.

4. Ensure server responses do not vary in timing or behavior based on whether the signature is correct beyond the constant‑time comparison. Avoid branching logic that performs extra checks only on success, and standardize error responses and HTTP status codes for authentication failures.

By combining constant‑time verification, strict rate limiting, and deterministic handling of authentication outcomes, you reduce the surface that enables dictionary attacks against HMAC‑protected endpoints in Echo Go. These measures complement the scanning capabilities of middleBrick, which can surface missing rate limits and inconsistent authentication behavior in both the OpenAPI spec and runtime findings.