Llm Data Leakage in Gin with Hmac Signatures

When building HTTP APIs in Go with the Gin framework, developers sometimes add HMAC signatures to verify request integrity and origin. If implementation details are handled incorrectly, this pattern can contribute to LLM data leakage by exposing secret material or structured data in a way that language models can inadvertently surface through generated responses or logs. middleBrick specifically tests for scenarios where AI-related endpoints or debug handlers leak information about HMAC validation, secrets, or request metadata.

In Gin, a common pattern is to compute an HMAC over selected request components (such as headers, timestamps, or payloads) and require the client to provide a signature in a header. If the server returns verbose errors, echo-back of signature-related values, or exposes the signing key through logs or debug routes, an attacker or an LLM-integrated endpoint might extract that secret. middleBrick’s LLM/AI Security checks look for system prompt leakage and active prompt injection, which can occur when an API handler includes raw request data or secret-derived values in messages passed to an LLM or returned to clients. For example, echoing an HMAC value or including secret-derived nonces in LLM context increases the chance that the model output reveals sensitive material.

Another vector specific to the combination of Gin, HMAC, and LLM endpoints is the presence of unauthenticated handlers that process user input used to compute or verify signatures. If such handlers reflect signature mismatches in error messages that include portions of the provided signature or metadata, and those messages are passed to an LLM for processing or displayed in UI logs, confidential patterns or key material may be disclosed. middleBrick’s active prompt injection probes include data exfiltration tests designed to see whether crafted inputs can trick handlers into returning secret-related data through LLM outputs or logs. Additionally, if an API exposes an endpoint that returns model context including headers or HMAC metadata without proper filtering, the output may contain sensitive information that becomes reachable through LLM tooling or logs.

Because Gin applications often compose multiple handlers and middleware, improper scoping of HMAC verification can lead to inconsistent enforcement. If some routes validate signatures while others do not, an attacker may pivot to a less-protected endpoint that echoes request data into an LLM integration, inadvertently exposing signed values. middleBrick’s scans include checks for unauthenticated LLM endpoints and excessive agency patterns, which help surface cases where unsigned or weakly protected routes coexist with LLM interactions. This is particularly important when APIs accept user-controlled content that is later used to generate model inputs containing HMAC-related fields.

Remediation for this scenario focuses on ensuring that HMAC handling never exposes secrets or signature values through responses, logs, or LLM contexts. All error messages should be generic, avoiding reflection of signature or key material. If an API integrates with LLM endpoints, ensure that request data sent to the model is sanitized to remove HMAC values and secret-derived fields. middleBrick’s findings include specific guidance on input validation and data exposure to help developers redesign flows so that sensitive components do not propagate into model prompts or uncontrolled outputs.

To securely use HMAC signatures in Gin without risking LLM data leakage, keep secret material server-side, avoid echoing signature values, and ensure that error messages do not reveal validation details. Below is a concise, real-world example that demonstrates a safe pattern for HMAC verification in Gin handlers.

package main

import (
	"crypto/hmac"
	"crypto/sha256"
	"encoding/hex"
	"net/http"
	"strings"

	"github.com/gin-gonic/gin"
)

// getSecret retrieves the signing key from a secure source (e.g., environment variable).
// Never echo this value in responses or logs.
func getSecret() []byte {
	// In production, load from a secure configuration or secret manager.
	return []byte("super-secret-key-keep-safe")
}

// computeHMAC returns the hex-encoded HMAC-SHA256 of the message using the key.
func computeHMAC(message string, key []byte) string {
	h := hmac.New(sha256.New, key)
	h.Write([]byte(message))
	return hex.EncodeToString(h.Sum(nil))
}

// verifyHMAC returns true if the provided signature matches the expected HMAC.
func verifyHMAC(message, receivedSig string, key []byte) bool {
	expected := computeHMAC(message, key)
	return hmac.Equal([]byte(expected), []byte(receivedSig))
}

func main() {
	r := gin.Default()

	// Apply HMAC verification middleware to selected routes.
	r.Use(func(c *gin.Context) {
		// Skip verification for public endpoints if needed.
		if c.Request.URL.Path == "/public/health" {
			c.Next()
			return
		}

		key := getSecret()
		// Construct the signed payload from headers/body as agreed with clients.
		// Example: signed headers and a timestamp to prevent replay.
		signedHeader := c.GetHeader("X-API-Signature")
		signedTimestamp := c.GetHeader("X-API-Timestamp")
		if signedHeader == "" || signedTimestamp == "" {
			c.AbortWithStatusJSON(http.StatusUnauthorized, gin.H{"error": "missing signature or timestamp"})
			return
		}

		// Build the exact string that was signed on the client.
		// This must match the client’s signing logic exactly.
		payload := signedTimestamp + "." + c.Request.URL.Path
		if !verifyHMAC(payload, signedHeader, key) {
			// Generic error to avoid leaking whether the timestamp, path, or signature was incorrect.
			c.AbortWithStatusJSON(http.StatusUnauthorized, gin.H{"error": "invalid request"})
			return
		}

		// Optional: basic replay protection using a short cache of recent timestamps.
		// Implement as appropriate for your threat model.

		c.Next()
	})

	// Example route that safely uses verified context without exposing HMAC details.
	r.GET("/secure/data", func(c *gin.Context) {
		// At this point, HMAC verification succeeded.
		// Do not include signature or secret-derived values in the response.
		c.JSON(http.StatusOK, gin.H{"status": "ok"})
	})

	// Example of an LLM-friendly route that sanitizes output.
	// Ensure that any data forwarded to an LLM excludes HMAC values.
	r.GET("/llm/query", func(c *gin.Context) {
		userQuery := c.Query("q")
		// Remove or redact any HMAC-related metadata before sending to LLM.
		// For instance, strip headers that should not be part of the prompt.
		prompt := "User query: " + userQuery
		// callLLM(prompt) would be implemented separately; ensure no HMAC values are included.
		c.JSON(http.StatusOK, gin.H{"prompt_sent_to_llm": prompt})
	})

	r.Run(":8080")
}

Key points in this example:

• Secrets are loaded server-side and never returned to the client or logged.
• Error messages are generic to prevent attackers from distinguishing between missing, malformed, or invalid signatures.
• The signed payload is constructed from components agreed upon between client and server; any mismatch in construction leads to rejection without details.
• Routes that interact with LLMs avoid passing HMAC values or secret-derived fields into prompts, reducing the risk of LLM data leakage through generated outputs or logs.

For teams using the middleBrick ecosystem, the CLI tool (middlebrick scan <url>) can validate that endpoints do not echo signature material and that LLM routes follow safe input practices. The GitHub Action can enforce a minimum security score before deployment, while the MCP Server allows AI coding assistants to surface insecure HMAC usage patterns during development.