Prompt Injection in Buffalo with Bearer Tokens

Buffalo is a Go web framework commonly used to build APIs and web applications. When a Buffalo application exposes an endpoint that accepts user-supplied input and forwards it to an LLM without strict validation or isolation, prompt injection becomes possible. Adding Bearer token handling to such endpoints can inadvertently widen the attack surface if authorization and prompt construction are not strictly separated.

Consider a scenario where an authenticated request includes an Authorization header with a Bearer token, and the application uses token-derived values (such as user ID or roles) to influence the LLM prompt. If user-controlled parameters are concatenated into the system or user prompt, an attacker can inject instructions that alter the LLM behavior. For example, a crafted input like admin'; system('echo leaked') could be appended to a query that is then inserted into the prompt, leading to unintended instruction overrides or data exfiltration.

In Buffalo, this often manifests in handlers where the Bearer token is parsed and then combined with dynamic content before being sent to the LLM. Because the token is treated as trusted contextual data, developers may fail to sanitize or strictly scope the user-supplied portions of the prompt. This trust boundary violation means that even though authentication succeeds, the constructed prompt may execute attacker-controlled instructions. The LLM security check performed by tools like middleBrick specifically targets these patterns by probing for system prompt extraction, instruction override, and DAN jailbreak techniques to verify whether user input can influence the model’s behavior.

Moreover, when an endpoint returns token-sensitive information (such as internal identifiers or scoped claims), improper prompt usage can lead to output leakage. The LLM might inadvertently include the Bearer token or associated metadata in its response if the prompt does not explicitly constrain output format and content. Output scanning is essential to detect PII, API keys, or executable code in LLM responses, ensuring that tokens and sensitive data are not exposed through generated text.

middleBrick’s LLM/AI Security checks help identify these risks by running sequential probes, including system prompt extraction and cost exploitation tests, against endpoints that integrate LLM functionality. By analyzing how prompts are built from request data and tokens, the scanner detects whether user input can escape intended boundaries. This is particularly important in Buffalo applications where route parameters, query strings, or JSON bodies may be improperly interpolated into prompt templates.

To secure Buffalo applications that use Bearer tokens and LLM integration, enforce strict separation between authorization data and prompt content. Never directly embed token-derived values or user input into system or user prompts. Instead, treat all external data as untrusted and validate or transform it before inclusion.

Below are concrete code examples demonstrating secure handling in Buffalo. The first example shows a vulnerable pattern where a Bearer token and user input are carelessly combined with the prompt:

app.get("/vulnerable", func(c buffalo.Context) error {
    token := c.Request().Header.Get("Authorization")
    // token format: Bearer <token>
    userQuery := c.Param("query")
    // Vulnerable: user input and token influence prompt directly
    prompt := fmt.Sprintf("System role: assistant. User: %s. Query: %s. Token scope: %s", "human", userQuery, token)
    llmResponse, err := callLLM(prompt)
    if err != nil {
        return c.Error(500, errors.WithStack(err))
    }
    return c.Render(200, r.JSON(llmResponse))
})

In this example, the Bearer token is parsed and inserted into the prompt, creating a path for prompt injection and potential token leakage. An attacker could supply input designed to alter the role or append malicious instructions.

The secure alternative is to sanitize inputs and keep authorization data out of the prompt. Use the Bearer token only for access control and scope validation, and construct a static, bounded prompt for the LLM:

app.get("/secure", func(c buffalo.Context) error {
    authHeader := c.Request().Header.Get("Authorization")
    if !strings.HasPrefix(authHeader, "Bearer ") {
        return c.Error(401, errors.New("unauthorized"))
    }
    token := strings.TrimPrefix(authHeader, "Bearer ")
    // Validate token scope via your auth provider; do not embed in prompt
    if !isValidScope(token, "llm_access") {
        return c.Error(403, errors.New("forbidden"))
    }
    userQuery := c.Param("query")
    // Sanitize and constrain user input
    safeQuery := sanitizeInput(userQuery)
    // Static prompt with no external injection points
    prompt := "You are a helpful assistant. Answer the user's question concisely."
    llmResponse, err := callLLMWithContext(prompt, safeQuery)
    if err != nil {
        return c.Error(500, errors.WithStack(err))
    }
    return c.Render(200, r.JSON(llmResponse))
})

In the secure handler, the Bearer token is used strictly for authentication and scope checks. The prompt does not interpolate user input or token values. User input is sanitized, and the LLM call uses a separate context mechanism that avoids string interpolation into the system prompt. This pattern minimizes the risk of prompt injection and prevents accidental exposure of tokens in LLM responses.

Additionally, enable output scanning as part of your testing regimen. Configure middleBrick to analyze responses for API keys, PII, and executable code, ensuring that even if a vulnerability exists, leaked tokens are quickly identified. Regularly review route definitions and middleware to confirm that authorization logic remains independent from prompt construction.