Llm Data Leakage in Buffalo with Bearer Tokens

LLM data leakage in Buffalo with Bearer tokens occurs when an API endpoint protected by a Bearer token scheme exposes language model functionality without adequate authorization checks, allowing an unauthenticated or insufficiently scoped client to trigger responses that reveal sensitive information. Buffalo is a Go web framework commonly used to build APIs, and it often relies on middleware to validate Authorization headers before routing requests to handlers that may call an LLM client.

In a vulnerable setup, a route such as /api/chat might accept a Bearer token in the Authorization header but fail to enforce scope-based validation or assume the token alone guarantees permission to invoke the LLM endpoint. If the handler passes user input directly to a model without output filtering, the LLM may return training data snippets, system messages, or internal logic that include secrets embedded in prompts. Because Buffalo applications frequently compose middleware chains, missing or misconfigured token validation can let an attacker reach the LLM handler through a public route, especially when CORS or route-level guards are not strictly enforced.

During black-box scanning, middleBrick tests unauthenticated attack surfaces and includes LLM/AI Security checks that specifically probe for system prompt leakage, active prompt injection, and output exposure. For Buffalo APIs using Bearer tokens, the scanner verifies whether token validation is consistently applied across all routes that interact with LLM backends. It checks whether tokens are validated before prompt assembly, whether scopes or roles are inspected to restrict LLM access, and whether responses are scanned for PII, API keys, or executable code. A common finding is that token validation occurs at the router level but is bypassed for specific paths, or that tokens are accepted but not verified against an identity provider, enabling an attacker to reach unprotected LLM endpoints.

Real-world risks include exposure of system prompts that contain instructions like You are a support bot for PCI-compliant transactions, which can reveal compliance boundaries; leakage of internal tool usage patterns that hint at business logic; and output that inadvertently includes data seen during training or retrieved from insecure memory. Because Buffalo routes often map cleanly to resource-based authorization models, missing property-level checks can turn a seemingly harmless chat endpoint into a channel for data exfiltration when combined with weak Bearer token enforcement.

middleBrick’s LLM/AI Security checks run five sequential probes against such endpoints, including system prompt extraction and output scanning, to identify whether Bearer token protections are insufficient or inconsistently applied. The scanner cross-references these runtime findings with OpenAPI specifications, resolving $ref definitions to confirm whether security schemes are declared for LLM-related paths and whether those declarations match runtime behavior in Buffalo implementations.

Remediation focuses on ensuring Bearer token validation is strict, consistent, and scoped before any LLM interaction in Buffalo handlers. Below are concrete, syntactically correct examples that demonstrate secure patterns.

1) Validate Bearer token before LLM call

Use middleware to extract and verify the token, and only then construct the prompt. This example shows a protected /api/chat route in Buffalo that validates the Authorization header and checks a required scope before proceeding.

// middleware/auth.go
package middleware

import (
    "net/http"
    "strings"
)

func RequireAuth(requiredScope string) func(http.Handler) http.Handler {
    return func(next http.Handler) http.Handler {
        return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
            auth := r.Header.Get("Authorization")
            if auth == "" {
                http.Error(w, `{"error": "authorization header required"}`, http.StatusUnauthorized)
                return
            }
            parts := strings.Split(auth, " ")
            if len(parts) != 2 || strings.ToLower(parts[0]) != "bearer" {
                http.Error(w, `{"error": "invalid authorization format"}`, http.StatusUnauthorized)
                return
            }
            token := parts[1]
            // Validate token and scopes with your identity provider
            scopes, valid := validateToken(token)
            if !valid || !hasScope(scopes, requiredScope) {
                http.Error(w, `{"error": "insufficient scope"}`, http.StatusForbidden)
                return
            }
            next.ServeHTTP(w, r)
        })
    }
}

// handlers/chat.go
package handlers

import (
    "encoding/json"
    "net/http"
    "yourproject/middleware"
)

func ChatHandler(w http.ResponseWriter, r *http.Request) {
    var req struct {
        Message string `json:"message"`
    }
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        http.Error(w, `{"error": "invalid request body"}`, http.StatusBadRequest)
        return
    }
    // Only build and send prompt after token validation
    prompt := buildPrompt(req.Message)
    response, err := callLLM(prompt)
    if err != nil {
        http.Error(w, `{"error": "llm call failed"}`, http.StatusInternalServerError)
        return
    }
    json.NewEncoder(w).Encode(response)
}

func buildPrompt(userMessage string) string {
    // Avoid including sensitive context in the prompt
    return "You are a support assistant. Respond concisely. User: " + userMessage
}

func callLLM(prompt string) (string, error) {
    // Implement your LLM client call here
    return "assistant response", nil
}

// main.go
func main() {
    r := buffalo.New()
    r.Group("/api", func(api *RouteGroup) {
        api.Use(middleware.RequireAuth("llm:chat"))
        api.Post("/chat", handlers.ChatHandler)
    })
    r.Serve()
}

2) Enforce scope-based access for LLM endpoints

Define granular scopes (e.g., llm:chat, llm:admin) and ensure token validation checks them. The following snippet demonstrates scope verification inside the middleware, preventing users with only llm:read from invoking the chat handler.

// middleware/scope.go
package middleware

func HasScope(scopes []string, target string) bool {
    for _, s := range scopes {
        if s == target {
            return true
        }
    }
    return false
}

// validateToken is a stub; integrate with your auth provider
func validateToken(token string) ([]string, bool) {
    // Return scopes from token introspection or JWT claims
    // Example: return []string{"llm:chat", "profile:read"}, true
    return []string{"llm:chat"}, true
}

3) Secure output handling and prompt hygiene

Ensure LLM responses are scanned before returning to the client. Do not echo raw user input into prompts, and avoid including secrets in prompt templates that could be reflected in outputs.

// handlers/chat.go (extended)
import "regexp"

var unsafeRegex = regexp.MustCompile(`(?i)(api_key|secret|token)=[^&\s]+`)

func SanitizeOutput(resp string) string {
    return unsafeRegex.ReplaceAllString(resp, "[REDACTED]")
}

// Inside ChatHandler after callLLM:
sanitized := SanitizeOutput(response)
json.NewEncoder(w).Encode(map[string]string{"response": sanitized})

4) Apply consistent middleware across all LLM routes

In Buffalo, use route groups to apply RequireAuth to all endpoints that interact with LLMs. Avoid allowing public access to any handler that constructs prompts or calls external model services.

r.Group("/api/llm", func(llm *RouteGroup) {
    llm.Use(middleware.RequireAuth("llm:chat"))
    llm.Post("/chat", handlers.ChatHandler)
    llm.Post("/assist", handlers.AssistHandler)
})

These patterns ensure that Bearer tokens are validated, scopes are enforced, and LLM outputs are sanitized, reducing the risk of data leakage in Buffalo applications.