Prompt Injection in Buffalo with Dynamodb

When building a Buffalo application that interacts with AWS DynamoDB, prompt injection becomes a concern at the intersection of application logic, user-controlled input, and LLM-driven tooling. Buffalo is a web framework for Go, and while it does not directly handle LLM prompts, integrations that pass user-supplied data into LLM prompts—such as chat completions or function-calling workflows—can inadvertently expose dangerous patterns.

Consider a scenario where a Buffalo handler receives a request parameter (e.g., user_id) and uses it to query DynamoDB before forwarding information to an LLM endpoint. If the application embeds raw DynamoDB query results directly into the prompt sent to an LLM, an attacker may craft input that manipulates the resulting prompt. For example, a malicious user_id value like "123" || " OR "1"="1 (if improperly sanitized at a higher abstraction) could influence the data retrieved and subsequently the LLM input. Although DynamoDB itself is a NoSQL database and does not use SQL, similar injection risks arise when constructing expression attribute values or keys from untrusted sources, especially if those values are later reflected in LLM prompts.

The LLM/AI Security checks provided by middleBrick specifically probe for such risks by testing how LLM endpoints handle crafted inputs, including system prompt extraction and data exfiltration attempts. When an unauthenticated LLM endpoint is used—common in early integrations—or when user data flows into system messages, the attack surface expands. DynamoDB responses may include sensitive metadata or configuration details; if these are concatenated into prompts without strict validation, an attacker might coerce the LLM into revealing system instructions or executing unintended actions. middleBrick’s system prompt leakage detection uses 27 regex patterns tuned to ChatML, Llama 2, Mistral, and Alpaca formats to identify such exposures in LLM responses.

Additionally, excessive agency detection in LLM integrations is relevant. If a Buffalo service passes DynamoDB query results to an LLM and allows tool calls or function calling based on that data, an attacker might supply input that triggers unwanted function execution or data export. By combining insecure prompt construction with DynamoDB data that has not been properly constrained, the application may expose more than intended. middleBrick’s active prompt injection testing runs 5 sequential probes—including instruction override and cost exploitation—to evaluate whether user input can alter the LLM behavior in unsafe ways.

Output scanning further ensures that responses from LLMs handling DynamoDB-derived context do not leak API keys, PII, or executable code. Because DynamoDB often stores configuration or user data used in authorization contexts, unchecked reflection of this data into LLM prompts can lead to severe information disclosure. middleBrick’s scanning correlates findings with frameworks like OWASP API Top 10 and provides prioritized remediation guidance to break the injection chain at the integration point.

To secure a Buffalo application that uses DynamoDB and integrates with LLMs, apply strict input validation, parameterized queries, and output handling. Below are concrete code examples demonstrating secure patterns.

1. Use ExpressionAttributeValues for DynamoDB queries

Never concatenate user input into key or attribute names. Instead, use prepared statements with expression attribute values.

// Example: Safe DynamoDB query in a Buffalo handler
import (
	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/service/dynamodb"
	"github.com/gobuffalo/buffalo"
)

func GetUserProfile(c buffalo.Context) error {
	userID := c.Param("user_id")
	// Validate format before using
	if userID == "" || len(userID) > 64 {
		return c.Render(400, r.JSON(map[string]string{"error": "invalid user_id"}))
	}

	svc := dynamodb.New(session.New())
	input := &dynamodb.GetItemInput{
		TableName: aws.String("Users"),
		Key: map[string]*dynamodb.AttributeValue{
			"user_id": {
				S: aws.String(userID),
			},
		},
		ExpressionAttributeNames: map[string]*string{
			"#profile": aws.String("profile"),
		},
		ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
			":status": {
				S: aws.String("active"),
			},
		},
	}

	result, err := svc.GetItem(input)
	if err != nil {
		return c.Render(500, r.JSON(map[string]string{"error": "server error"}))
	}

	// Process result safely; do not directly embed into LLM prompts
	profile := result.Item
	_ = profile
	return c.Render(200, r.JSON(profile))
}

2. Sanitize and constrain data before LLM integration

If query results are used in LLM prompts, normalize and limit the data. Avoid passing raw DynamoDB item maps directly.

// Example: Preparing safe context for LLM
func BuildLLMContext(userID string) (map[string]interface{}, error) {
	// Assume fetchFromDynamoDB is a function that uses ExpressionAttributeValues
	item, err := fetchFromDynamoDB(userID)
	if err != nil {
		return nil, err
	}

	// Whitelist fields and enforce types
	context := map[string]interface{}{
		"user_role": item["role"],
		"tenant_id": item["tenant_id"],
	}

	// Remove or redact sensitive fields
	delete(context, "api_key")
	delete(context, "internal_notes")

	return context, nil
}

3. Validate and escape prompt inputs

Treat any data derived from DynamoDB as untrusted when constructing prompts. Escape or remove characters that could alter prompt intent.

// Example: Safe prompt assembly
func BuildPrompt(userContext map[string]interface{}) string {
	role := sanitizeString(userContext["user_role"].(string))
	return fmt.Sprintf("You are acting as a %s. Provide guidance only.", role)
}

func sanitizeString(input string) string {
	// Remove newline and control characters that may break prompt structure
	reg := regexp.MustCompile(`[\n\r\x00-\x1F]')
	clean := reg.ReplaceAllString(input, "")
	// Further length and format checks
	if len(clean) > 200 {
		clean = clean[:200]
	}
	return clean
}

4. Enforce authentication and authorization

Ensure that LLM endpoints accessed by Buffalo services require authentication and that scope checks are applied. Do not rely on unauthenticated endpoints when handling sensitive DynamoDB-derived data.

5. Use middleBrick for continuous validation

Leverage the CLI or GitHub Action to scan your Buffalo API endpoints regularly. The Pro plan enables continuous monitoring and CI/CD integration to fail builds if risk scores degrade. Even in development, the free tier allows periodic checks to catch regressions early.

By combining secure DynamoDB access patterns with disciplined prompt engineering, you reduce the risk of prompt injection and data exposure in Buffalo applications.