Prompt Injection Indirect in Buffalo (Go)

Prompt injection indirect in a Buffalo application written in Go occurs when user-controlled input influences the construction or selection of prompts that are later sent to an LLM endpoint, without direct injection into the prompt template itself. In Buffalo, this commonly arises when route parameters, query strings, or form values are used to select system instructions, context files, or LLM model configurations. Because Buffalo is a Go-based web framework, the flow typically involves building an HTTP handler that reads request data, assembles a prompt, and forwards it to an unauthenticated LLM endpoint for processing. If the handler uses unchecked user input to pick a prompt variant or to adjust parameters passed to the LLM, an attacker can manipulate the effective prompt by providing specially crafted inputs that change behavior in unintended ways.

Consider a scenario where a Buffalo handler uses a URL parameter to choose a context file for an LLM call. The Go code might concatenate the parameter into a file path or select from a map of predefined prompts. Because the selection logic is based on user data, an indirect prompt injection occurs when the attacker influences which prompt is used, rather than embedding new prompt content directly. This can change the system role instructions, alter the expected output format, or enable data exfiltration if the selected prompt is less restrictive. Because the LLM endpoint is unauthenticated in some deployments, the manipulated prompt is executed without additional safeguards, making the attack feasible without credentials.

Another vector specific to Go integrations involves environment variables or configuration templates that are populated from request context. If a Buffalo handler incorporates values from headers or query parameters into configuration used for LLM requests, an attacker can indirectly modify system-level instructions by controlling those values. For example, a handler might build a request body for an LLM endpoint by merging user-supplied fields into a base template. Even if the template itself is static, the merged data can shift the semantic meaning of the prompt, leading to jailbreak-like outcomes or unintended tool usage. The indirect nature means the malicious input does not appear inside the prompt text but changes how the prompt is interpreted or executed by the LLM, which is detectable through output scanning for PII, API keys, or executable code as part of the LLM/AI Security checks.

Using middleBrick to scan such a Buffalo endpoint can reveal these indirect prompt injection risks. The scanner performs active prompt injection testing, including system prompt extraction and instruction override probes, against the reachable LLM endpoint. Because the scan is unauthenticated and runs in 5–15 seconds, it can surface cases where user-controlled inputs affect the selected or constructed prompt. The LLM/AI Security checks also inspect responses for leaked system prompts, PII, or API keys, helping confirm whether indirect injection altered the behavior of the LLM in a way that exposes sensitive instructions or enables data exfiltration.

Because Buffalo applications in Go often rely on clean URL routing and flexible parameter handling, developers must ensure that any data used to route, select, or configure LLM interactions is strictly validated and isolated from prompt construction logic. Relying on output scanning and active prompt injection testing, as provided by middleBrick, is essential to detect indirect prompt injection paths that are not obvious in the source code but can be triggered through seemingly benign inputs.

Remediation for prompt injection indirect in Buffalo with Go focuses on strict input validation, avoiding user data in prompt selection or LLM configuration, and isolating external influences from the prompt construction process. The following examples demonstrate secure patterns that prevent indirect manipulation while remaining compatible with Buffalo’s conventions.

1. Avoid using user input to select prompts or files

Do not derive file paths or prompt keys from request parameters. Instead, use a predefined allowlist and map user intent to a fixed set of options.

// Unsafe: using user input to select a prompt file
func (v MyController) ShowContext(c buffalo.Context) error {
    name := c.Param("context")
    path := filepath.Join("prompts", name + ".txt")
    data, err := os.ReadFile(path)
    if err != nil {
        return c.Render(400, r.String("invalid context"))
    }
    // use data in LLM request…
    return nil
}

// Secure: allowlist-based selection
var allowedContexts = map[string]string{
    "summary": "prompts/summary.txt",
    "detail":  "prompts/detail.txt",
}

func (v MyController) ShowContext(c buffalo.Context) error {
    key := c.Param("context")
    path, ok := allowedContexts[key]
    if !ok {
        return c.Render(400, r.String("invalid context"))
    }
    data, err := os.ReadFile(path)
    if err != nil {
        return c.Render(500, r.String("failed to load context"))
    }
    // use data in LLM request…
    return nil
}

2. Do not merge user input into LLM request bodies

Construct the request body for the LLM endpoint with static structure and validated, non-prompt fields. Avoid inserting raw user data into prompt-like fields.

// Unsafe: injecting user input into the prompt field
payload := map[string]string{
    "prompt": "You are a helpful assistant. " + c.Param("user_instruction"),
    "model":  "llm-model",
}

// Secure: keep user input as a separate, validated parameter
userInstruction, err := validateInstruction(c.Param("user_instruction"))
if err != nil {
    return c.Render(400, r.String("invalid instruction"))
}
payload := map[string]interface{}{
    "system": "You are a helpful assistant.",
    "user_request": userInstruction,
    "model": "llm-model",
}

3. Validate and constrain configuration derived from requests

If your handler adjusts model or endpoint settings based on request data, enforce strict constraints and avoid passing raw values to LLM configuration.

// Unsafe: using query parameter to set temperature or model
var req struct {
    Temperature float64 `json:"temperature"`
    Model       string  `json:"model"`
}
if bindErr := c.Bind(&req); bindErr != nil {
    return c.Render(400, r.String("invalid request"))
}

// Secure: clamp values and use defaults
if req.Temperature < 0.0 {
    req.Temperature = 0.5
}
if req.Temperature > 2.0 {
    req.Temperature = 2.0
}
allowedModels := map[string]bool{"llm-model": true}
if !allowedModels[req.Model] {
    req.Model = "llm-model"
}

4. Use structured outputs and inspect LLM responses

Enable output scanning for PII, API keys, and executable code by integrating middleBrick’s LLM/AI Security checks into your testing pipeline. In Go tests, you can call a verification helper on LLM responses to detect unintended disclosures before deployment.

func assertLLMResponseSafe(t *testing.T, resp *llm.Response) {
    t.Helper()
    if strings.Contains(resp.Text, "-----BEGIN PRIVATE KEY-----") {
        t.Fatal("response contains API key")
    }
    if matched, _ := regexp.MatchString(`\b\d{10,}\b`, resp.Text); matched {
        t.Fatal("response may contain PII or sensitive numbers")
    }
}

By applying these Go-specific fixes, you ensure that user input cannot indirectly alter prompt behavior or LLM configuration. Combine these practices with periodic scans using middleBrick’s CLI or GitHub Action to continuously verify that indirect prompt injection paths are not present in your Buffalo application.