Prompt Injection in Buffalo with Basic Auth

Prompt injection in Buffalo when Basic Auth is used arises because authentication headers become part of the observable request surface that an LLM-facing endpoint may reflect or misuse. Buffalo’s strength as a Go web framework is its explicit handling of requests, but if you expose an endpoint that accepts user input and forwards it or includes it in contexts sent to an LLM, the Authorization header can unintentionally influence the prompt sent to the model.

Consider a handler that calls an external LLM and builds a system prompt from request data. If the handler includes the value of the Authorization header (or derived user identity from it) in the prompt without strict validation and escaping, an attacker authenticating with a crafted token can inject instructions. For example, a token containing sequence patterns that resemble prompt directives may cause the LLM to change role, ignore prior instructions, or exfiltrate data. This is especially relevant for unauthenticated LLM endpoint detection: if the LLM endpoint itself does not require its own auth and the application leaks identity via the prompt, the attack path bypasses the application’s Basic Auth while still abusing the context construction logic.

In a black-box scan, middleBrick tests such scenarios by probing endpoints that accept user-controlled data and interact with LLMs. It checks for system prompt leakage and runs sequential active probes: system prompt extraction, instruction override, DAN jailbreak, data exfiltration, and cost exploitation. When Basic Auth is present but the application does not sanitize the Authorization header before using it in LLM prompts, these probes can succeed, revealing that user-controlled identity can override intended instructions. The scanner also flags outputs containing API keys or PII, which may occur if injected instructions cause the LLM to echo headers or sensitive data. Because the scan tests the unauthenticated attack surface, it does not require credentials to find these logic flaws in prompt construction.

Real-world patterns include concatenating the Authorization header directly into a system message or using user identity to select a prompt template without escaping. For instance, if you do not validate the header value, an attacker token like Basic d3Jvbmc6cGFzcw== (decoded as wrong:pass) might be placed in a role or instruction, causing the model to misinterpret the intended behavior. The vulnerability is not in Basic Auth itself, but in how the framework integrates authenticated identity into LLM interactions. Proper mitigation requires strict separation of authentication from prompt content and rigorous input validation for any data that reaches the LLM.

To secure Buffalo applications that use Basic Auth and interact with LLMs, ensure that authentication data never pollutes prompt construction. Validate and sanitize all inputs, and avoid using raw headers in system or user messages. Below are concrete code examples demonstrating safe practices.

Safe handler structure without leaking auth into prompts

Use a dedicated context that excludes authentication metadata when building prompts. Decode the username and password only for access control, and do not propagate them to the LLM layer.

package controllers

import (
	"net/http"
	"strings"

	"github.com/gobuffalo/buffalo"
)

// authenticate decodes Basic Auth and validates credentials.
func authenticate(r *http.Request) (string, bool) {
	hdr := r.Header.Get("Authorization")
	if hdr == "" || !strings.HasPrefix(hdr, "Basic ") {
		return "", false
	}
	// Decode base64 payload (omitted for brevity; use encoding/base64 in production).
	payload, ok := decodeBasicAuth(hdr) // implement safely
	if !ok {
		return "", false
	}
	// Expected format: username:password
	parts := strings.SplitN(payload, ":", 2)
	if len(parts) != 2 {
		return "", false
	}
	username, password := parts[0], parts[1]
	// Validate against your store; return a safe user identifier.
	if validUser(username, password) {
		return username, true
	}
	return "", false
}

// handler that calls an LLM without including auth in the prompt.
func llmHandler(c buffalo.Context) error {
	username, ok := authenticate(c.Request())
	if !ok {
		return c.Render(401, r.JSON(map[string]string{"error": "unauthorized"}))
	}

	// Build prompt without authentication data.
	userMessage := c.Params().Get("message")
	if userMessage == "" {
		return c.Render(400, r.JSON(map[string]string{"error": "message required"}))
	}

	// Construct safe system prompt.
	systemPrompt := "You are a helpful assistant. Respond concisely."
	// Use only sanitized user input.
	userPrompt := "User says: " + sanitizeInput(userMessage)

	// CallLLM is an abstraction over your LLM client.
	resp, err := CallLLM(systemPrompt, userPrompt)
	if err != nil {
		return c.Render(500, r.JSON(map[string]string{"error": "llm error"}))
	}

	return c.Render(200, r.JSON(map[string]string{"response": resp}))
}

func decodeBasicAuth(hdr string) (string, bool) {
	// Implement proper base64 decoding and avoid logging raw headers.
	return "", false
}

func validUser(username, password string) bool {
	// Implement secure check.
	return false
}

func sanitizeInput(s string) string {
	// Remove or escape characters that could alter prompt structure.
	return strings.TrimSpace(s)
}

Explicitly exclude auth from external calls

If you must pass user context to the LLM, do so via a controlled field (e.g., user ID) and not the raw Authorization header. Validate and constrain values rigorously.

// safeContext builds a user context map without exposing credentials.
func safeContext(r *http.Request) map[string]string {
	hdr := r.Header.Get("Authorization")
	if hdr == "" || !strings.HasPrefix(hdr, "Basic ") {
		return nil
	}
	payload, ok := decodeBasicAuth(hdr)
	if !ok {
		return nil
	}
	parts := strings.SplitN(payload, ":", 2)
	if len(parts) != 2 {
		return nil
	}
	// Only use username as a non-sensitive identifier.
	return map[string]string{"user_id": sanitizeInput(parts[0])}
}

Framework-level protections

Configure middleware to strip or ignore Authorization headers from being logged or forwarded to LLM-related handlers. Ensure that any feature that dynamically builds prompts uses a whitelist approach for allowed variables.

Testing and scanning

Use middleBrick to validate that your endpoints do not leak authentication details into LLM prompts. The scanner’s LLM/AI Security checks include system prompt leakage detection and active prompt injection testing, which will surface these issues when Basic Auth data influences the prompt.