Prompt Injection in Buffalo with Cockroachdb

In a Buffalo application that uses Cockroachdb as the backend datastore, prompt injection becomes a concern when user-influenced input is incorporated into prompts sent to an LLM endpoint without validation or sanitization. Buffalo is a web framework for Go that encourages straightforward request handling, where form values, query parameters, and headers are easily bound into handler structs. If these inputs are forwarded directly to an LLM—such as when constructing a system or user message for an AI feature—malicious payloads can alter the intended behavior of the model.

Consider a scenario where an endpoint accepts a user-supplied query to search documentation and includes that query in a prompt sent to an unauthenticated LLM endpoint. Because Buffalo does not inherently sanitize or escape content destined for external services, an attacker can inject instructions designed to change the model role, override prior guidance, or exfiltrate data. The presence of Cockroachdb does not cause the injection, but it often stores or indexes user-generated content that may later be embedded in prompts. If stored data is retrieved and used to build LLM messages without escaping newlines or special tokens, historical or third-party content can also act as an indirect injection vector.

The LLM/AI Security checks in middleBrick specifically probe this risk by testing for system prompt extraction, instruction override, DAN jailbreak, data exfiltration, and cost exploitation through sequential probes. These probes are valuable because they validate whether a Buffalo endpoint that references Cockroachdb data is resilient to crafted inputs that attempt to steer the model. For example, a user message that includes tokens like <<SYS>> or role overrides can shift the model into an unintended state, especially when the surrounding prompt is dynamically assembled from database rows.

Because middleBrick scans the unauthenticated attack surface and includes LLM/AI Security among its 12 parallel checks, it can identify whether an exposed endpoint is susceptible to prompt injection without requiring credentials. The scanner detects patterns such as missing input validation, improper escaping of newline characters, and usage of functions like token_count or tool-calling configurations that could enable excessive agency. In a Buffalo app interacting with Cockroachdb, this means ensuring that any data retrieved from the database is treated as untrusted input and is either sanitized or omitted from the prompt context before being sent to the model.

To reduce prompt injection risk in a Buffalo app using Cockroachdb, treat all data sourced from the database as potentially malicious when included in LLM prompts. Apply strict input validation and output encoding, and avoid dynamic assembly of system messages using raw user or database content. The following examples illustrate secure patterns.

First, use parameterized queries to retrieve only the necessary, pre-sanitized fields, and avoid concatenating raw rows into prompt strings. In Buffalo, you can bind query parameters safely with pop:

// Safe retrieval using placeholders
var docs []Document
if err := ptx.All(&docs, "SELECT id, title, summary FROM documents WHERE category = $1", category); err != nil {
    // handle error
}

Second, when constructing prompts, explicitly filter and transform database content. Strip or replace newline characters and control tokens that could be used for injection. For example, you can sanitize a summary before embedding it in a user message:

import "strings"

func sanitizeForPrompt(input string) string {
    // Remove control characters and extra newlines
    trimmed := strings.TrimSpace(input)
    cleaned := strings.ReplaceAll(trimmed, "\n", " ")
    // Optionally limit length
    if len(cleaned) > 500 {
        cleaned = cleaned[:500]
    }
    return cleaned
}

summary := sanitizeForPrompt(docs[i].Summary)
prompt := fmt.Sprintf("Summarize the following, but do not add new facts: %s", summary)

Third, avoid using database content to influence role or system instructions. Instead, keep system messages static and pass user data only in designated user roles. This minimizes the impact of any injected content stored in Cockroachdb:

systemMsg := "You are a helpful assistant that summarizes documents."
userMsg := fmt.Sprintf("User query: %s", sanitizeForPrompt(userQuery))

// Send systemMsg and userMsg to LLM without dynamic system overrides

Finally, leverage middleBrick’s CLI to validate your endpoints after changes. You can scan from terminal with the command middlebrick scan <url> to confirm that prompt injection probes no longer trigger on your Buffalo routes. In production, consider the Pro plan to enable continuous monitoring and GitHub Action integration so that future changes to Cockroachdb-driven prompts are automatically assessed before deployment.