Llm Data Leakage in Buffalo with Basic Auth

Buffalo is a popular Go web framework for rapid API development. When Basic Auth is used for route protection in Buffalo and an LLM endpoint is exposed or improperly handled, the combination can lead to LLM data leakage. This occurs when prompts, system instructions, or sensitive runtime data are inadvertently exposed through LLM-related handlers.

Basic Auth in Buffalo is typically implemented via middleware that checks the Authorization header. If an LLM endpoint (for example, a handler that forwards user input to an LLM service) does not properly validate or sanitize inputs and outputs, and if Basic Auth credentials are passed in headers that are logged, echoed, or reflected, sensitive information can leak. For instance, if a developer accidentally includes the Authorization header in debug output or LLM tool calls, credentials and prompt content may be exposed.

The LLM/AI Security checks in middleBrick specifically test for System Prompt Leakage using patterns matching ChatML, Llama 2, Mistral, and Alpaca formats. In a Buffalo app using Basic Auth, if an LLM handler echoes headers or includes them in prompt construction (e.g., prompt = "User: " + username + "\nPassword: " + password), system prompts or credentials can be extracted via crafted probes. middleBrick runs active prompt injection tests, including system prompt extraction and data exfiltration, against unauthenticated endpoints, which can surface leaks when Basic Auth is misconfigured or overly permissive.

Additionally, Output Scanning in middleBrick inspects LLM responses for PII, API keys, and executable code. In Buffalo, if LLM responses include sensitive data derived from Basic Auth context (such as usernames or roles) or if the app embeds credentials in generated text, these can be extracted by an attacker. Excessive Agency detection also flags patterns such as tool_calls or function calling that may expose internal routing or authentication logic through LLM outputs.

Because Buffalo does not enforce authentication on LLM endpoints by default, an unauthenticated attacker might probe an LLM route and trigger information disclosure via crafted inputs that cause the app to include Basic Auth credentials or prompt details in responses. The scanner’s unauthenticated attack surface testing highlights these risks by running probes without credentials, mimicking an external attacker’s view of the API surface.

Using middleBrick’s GitHub Action adds CI/CD checks that fail builds if the security score drops below a configured threshold, helping catch LLM data leakage early when combined with Basic Auth routes. The Dashboard allows teams to track findings over time and prioritize fixes for endpoints where authentication context intersects with LLM behavior.

To prevent LLM data leakage in Buffalo when using Basic Auth, ensure credentials are never echoed, logged, or embedded in LLM prompts or outputs. Apply strict middleware ordering and validate inputs to LLM handlers.

Example of insecure Basic Auth usage in Buffalo:

// insecure-buffalo.go: leaks credentials if used in prompts or logs
package actions

import (
  "github.com/gobuffalo/buffalo"
  "net/http"
)

func llmHandler(c buffalo.Context) error {
  user, pass, ok := c.Request().BasicAuth()
  if !ok {
    return c.Render(401, r.Text("Unauthorized"))
  }
  // Risk: using credentials in prompt construction
  prompt := "User: " + user + "\nQuery: " + c.Param("query")
  // Risk: logging credentials
  c.Logger().Infof("LLM request for %s with auth user %s", c.Param("query"), user)
  resp, err := callOpenAI(prompt)
  if err != nil {
    return c.Error(500, err)
  }
  // Risk: including auth-derived data in LLM response handling
  return c.Render(200, r.JSON(map[string]string{"reply": resp, "context_user": user}))
}

Secure remediation in Buffalo:

• Do not include Basic Auth credentials in prompts, logs, or response fields.
• Use middleware to enforce authentication without exposing credentials to handlers that interact with LLMs.
• Sanitize all inputs to LLM endpoints and avoid echoing headers in outputs.

Secure code example:

// secure-buffalo.go: avoids credential leakage
package actions

import (
  "github.com/gobuffalo/buffalo"
  "net/http"
)

// AuthMiddleware validates Basic Auth and sets a clean context value
func AuthMiddleware(next buffalo.Handler) buffalo.Handler {
  return func(c buffalo.Context) error {
    user, _, ok := c.Request().BasicAuth()
    if !ok {
      return c.Render(401, r.Text("Unauthorized"))
    }
    // Store only a user identifier, not credentials
    c.Set("current_user", user)
    return next(c)
  }
}

// llmHandlerSecure uses sanitized input and avoids credential leakage
func llmHandlerSecure(c buffalo.Context) error {
  user := c.Value("current_user").(string) // set by middleware
  query := c.Param("query")
  // Build prompt without credentials
  prompt := "Query: " + query
  // Avoid logging credentials; log only non-sensitive info
  c.Logger().Infof("LLM request for user %s", user)
  resp, err := callOpenAI(prompt)
  if err != nil {
    return c.Error(500, err)
  }
  // Do not include user or credentials in response
  return c.Render(200, r.JSON(map[string]string{"reply": resp}))
}

// Register routes with middleware
func App() *buffalo.App {
  r := buffalo.New(buffalo.Options{
    Env:         ENV,
    SessionStore: &sessions.NullStore{},
  })
  r.Use(AuthMiddleware)
  r.Get("/llm", llmHandlerSecure)
  return r
}

These changes ensure credentials are not passed into LLM prompts or outputs, reducing the risk of LLM data leakage. Combine these practices with middleBrick’s CLI scans to validate that endpoints remain secure after changes.