Llm Data Leakage in Gorilla Mux with Basic Auth

Gorilla Mux is a widely used HTTP router and matcher for Go that enables expressive routing patterns for REST and other APIs. When Basic Auth is used for access control, it typically relies on a fixed set of credentials passed in the Authorization header. In practice, developers sometimes attach user context or sensitive metadata to request-scoped objects or middleware values and later forward those values to LLM-enabled endpoints without scrubbing.

When an LLM endpoint is invoked from a Gorilla Mux handler that uses Basic Auth, the combination can unintentionally surface credentials or other sensitive data in LLM requests or responses. For example, if the handler passes the request context, headers, or route variables into a prompt without validation, credentials may appear in LLM input or output logs. An attacker who can influence the prompt or observe LLM outputs might extract credentials or other PII via crafted inputs that exploit how the handler builds prompts or calls external services.

LLM Data Leakage in this context refers to the risk that credentials, tokens, or other sensitive information are exposed through LLM interactions. With Basic Auth, the Authorization header value (typically base64-encoded user:password) must not be forwarded to or echoed by LLM endpoints. Gorilla Mux route variables (e.g., {id}) and headers may also be concatenated into prompts; if these values contain secrets or tokens, they can leak through LLM responses or be exfiltrated via prompt injection techniques.

The vulnerability is not inherent to Gorilla Mux or Basic Auth alone, but arises from insecure handling of request data when composing prompts or invoking LLM services. For instance, failing to remove or mask the Authorization header before sending user-supplied content to an LLM can result in credentials appearing in model outputs. Similarly, logging or caching prompts that include Basic Auth–derived values increases the attack surface.

middleBrick’s LLM/AI Security checks detect such exposures by scanning unauthenticated attack surfaces and inspecting whether credentials, PII, or secrets appear in LLM inputs or outputs. The scanner performs active prompt injection tests, including system prompt extraction and data exfiltration probes, to determine whether sensitive information can be coaxed into LLM responses. It also checks for System prompt leakage patterns and excessive agency behaviors that may inadvertently expose credentials through tool use or function calls.

In Gorilla Mux applications, mitigations include sanitizing all user input and request metadata before constructing prompts, explicitly removing or masking Authorization headers, and avoiding the inclusion of route variables or middleware values in LLM prompts unless strictly necessary and validated. Implementing strict output scanning for PII, API keys, and executable code further reduces the risk of leaked credentials being exposed through LLM channels.

To prevent LLM Data Leakage when using Basic Auth in Gorilla Mux, ensure that Authorization headers and any derived values are stripped from prompts and logs. Below are concrete code examples demonstrating secure handler patterns.

First, a helper to remove the Authorization header from incoming requests before they reach the LLM invocation logic:

// sanitize.go
package main

import (\n    "net/http"\n)\n\n// sanitizeRequest returns a copy of the request with the Authorization header removed.\nfunc sanitizeRequest(r *http.Request) *http.Request {\n    // Clone the request to avoid mutating the original.\n    reqClone := new(http.Request)\n    *reqClone = *r\n    reqClone.Header = make(http.Header)\n    for k, vv := range r.Header {\n        if k == "Authorization" {\n            continue // drop Authorization to avoid LLM leakage\n        }\n        reqClone.Header[k] = vv\n    }\n    return reqClone\n}\n

Second, a Gorilla Mux route that uses Basic Auth for access control but scrubs sensitive values before building the prompt:

// handlers.go
package main\n\nimport (\n    "encoding/base64"\n    "net/http"\n    "strings"\n    "github.com/gorilla/mux"\n)\n\n// llmHandler demonstrates safe invocation of an LLM endpoint.\nfunc llmHandler(w http.ResponseWriter, r *http.Request) {\n    vars := mux.Vars(r)\n    userID := vars["id"] // route variable\n\n    // Basic Auth credentials are available but must not be forwarded to LLM.\n    auth := r.Header.Get("Authorization")\n    var username string\n    if auth != "" && strings.HasPrefix(auth, "Basic ") {\n        payload, _ := base64.StdEncoding.DecodeString(auth[7:])\n        parts := strings.SplitN(string(payload), ":", 2)\n        if len(parts) == 2 {\n            username = parts[0]\n            // Do NOT include password in prompts or logs.\n        }\n    }\n
    // Build prompt using safe data only; exclude credentials and tokens.\n    prompt := buildPrompt(userID) // userID is safe if validated; do not include auth.\n
    // Use sanitized request when calling external LLM services.\n    req := sanitizeRequest(r)\n    // llmClient.Chat(req, prompt) — ensure client does not forward Authorization.\n    _ = req\n    _ = prompt\n    // Respond safely without echoing credentials.\n    w.Write([]byte("ok"))\n}\n
// buildPrompt constructs a prompt from safe inputs only.\nfunc buildPrompt(userID string) string {\n    // Validate and sanitize userID to prevent prompt injection.\n    return "Analyze request for user: " + userID\n}\n

Third, avoid logging or caching any values derived from the Authorization header. If you must log for debugging, explicitly redact credentials:

// logger.go
package main\n
import (\n    "log"\n    "strings"\n)\n\n// safeLog logs request metadata without exposing credentials.\nfunc safeLog(r *http.Request) {\n    auth := r.Header.Get("Authorization")\n    log.Printf("method=%s path=%s auth_present=%v", r.Method, r.URL.Path, auth != "")\n    // Never log the full Authorization header value.\n}

These patterns ensure that Basic Auth credentials are neither reflected in LLM prompts nor leaked via logs, reducing the chance of LLM Data Leakage. Combine these practices with output scanning for PII and API keys to further harden the system.