Hallucination Attacks in Buffalo with Dynamodb

A Hallucination Attack in the context of a Buffalo application using Amazon DynamoDB occurs when an AI component (e.g., an LLM-based helper or agent) generates plausible but false statements about data stored in DynamoDB, or is tricked into fabricating records, query logic, or access patterns. Because Buffalo encourages rapid prototyping and server-side rendering, developers may embed LLM outputs directly into HTML or API responses without validating them against the authoritative data in DynamoDB.

DynamoDB amplifies certain risks that enable or worsen hallucinations:

• Schema flexibility and sparse attributes: Items in a DynamoDB table can have varying attribute sets. An LLM may hallucinate attributes that do not exist, and a Buffalo handler that renders these attributes without checking the actual item shape will propagate false information to the client.
• Conditional and expression-based access patterns: DynamoDB’s ConditionExpression and expressions can be complex. If a Buffalo handler uses an LLM to construct or modify these expressions without strict validation, the LLM might generate conditions that do not align with the intended security boundaries, effectively hallucinating authorization rules.
• Pagination and LastEvalKey handling: DynamoDB paginated results require explicit handling of LastEvalKey. An LLM might hallucinate continuation tokens or assume a complete dataset was returned, causing Buffalo handlers to either miss records or present incomplete data as complete.
• Index usage and projection expressions: Queries against global or local secondary indexes return a subset of attributes. If an LLM assumes additional projected attributes are present, Buffalo code that renders those missing fields will expose hallucinated content.

In practice, this can manifest as a Buffalo controller calling an LLM to summarize user data from DynamoDB, where the LLM invents record counts, timestamps, or status values. Because Buffalo often delivers these summaries as JSON to frontend JavaScript, the hallucinations become actionable UI elements, increasing the impact. The attack surface is the unauthenticated or low-privilege API surface that the LLM endpoint can reach — for example, a public endpoint that queries DynamoDB via the AWS SDK and passes raw results to an LLM for natural language generation without verifying each item’s integrity.

To detect such issues, scanning tools perform active prompt injection tests (system prompt extraction, instruction override, DAN jailbreak, data exfiltration, cost exploitation) and output scanning for PII, API keys, and executable code. These checks help surface whether an LLM endpoint used by Buffalo is leaking system prompts or fabricating data that does not match DynamoDB records. Additionally, cross-referencing OpenAPI/Swagger definitions (2.0, 3.0, 3.1) with runtime findings ensures that declared responses align with what DynamoDB can realistically return.

Remediation focuses on ensuring Buffalo handlers validate every attribute returned from DynamoDB before presenting it to LLMs or rendering it in templates. Do not rely on LLM-generated summaries as the source of truth; instead use the LLM only as an assistant over verified data.

1. Validate item shape against a known schema

Define a struct that matches the expected DynamoDB attribute set and reject items that do not conform. This prevents hallucinated attributes from being used in rendering or further processing.

// app/models/user.go
package models

type User struct {
	ID       string
	Name     string
	Email    string
	Verified bool
}

// app/models/from_dynamodb.go
package models

import (
	"github.com/aws/aws-sdk-go/service/dynamodb"
	"github.com/aws/aws-sdk-go/aws"
)

func UserFromDynamoDB(item map[string]*dynamodb.AttributeValue) (*User, error) {
	if item == nil {
		return nil, ErrNotFound
	}
	// Ensure required fields exist; omit unexpected keys to prevent hallucination
	nameVal, okName := item["name"]
	emailVal, okEmail := item["email"]
	verifiedVal, okVerified := item["verified"]
	if !okName || !okEmail {
		return nil, ErrInvalidShape
	}
	return &User{
		ID:       aws.StringValue(item["id"]),
		Name:     aws.StringValue(nameVal),
		Email:    aws.StringValue(emailVal),
		Verified: aws.BoolValue(verifiedVal),
	}, nil
}

2. Use strict conditional expressions and avoid LLM-generated ConditionExpression

Construct ConditionExpression directly from validated inputs rather than allowing an LLM to compose it. If you must involve an LLM, treat its output as a suggestion and verify each clause.

// app/controllers/users.go
package controllers

import (
	"github.com/gobuffalo/buffalo"
	"github.com/aws/aws-sdk-go/service/dynamodb"
)

func ShowUser(c buffalo.Context) error {
	userID := c.Params().Get("id")
	// Direct, controlled condition; do not let LLM generate this string
	condition := &dynamodb.Condition{ // hypothetical SDK helper; adapt to aws/aws-sdk-go
		Comparison: aws.String("="),
		Attribute:  aws.String("id"),
		Value:      aws.String(userID),
	}
	// Use condition to fetch item; do not concatenate expressions from LLM output
	item, err := fetchItemWithCondition(condition)
	if err != nil {
		return c.Render(500, r.JSON(map[string]string{"error": "unable to fetch"}))
	}
	user, err := models.UserFromDynamoDB(item)
	if err != nil {
		return c.Render(400, r.JSON(map[string]string{"error": "invalid data shape"}))
	}
	return c.Render(200, r.JSON(user))
}

3. Handle pagination explicitly; do not trust LLM continuation tokens

DynamoDB pagination must be managed in Buffalo using SDK primitives. Do not allow an LLM to propose a LastEvalKey or exclusive start key; compute it from the SDK response and store it server-side if needed for stateful traversal.

// app/controllers/search.go
package controllers

import (
	"github.com/gobuffalo/buffalo"
	"github.com/aws/aws-sdk-go/service/dynamodb"
)

func SearchItems(c buffalo.Context) error {
	input := &dynamodb.QueryInput{
		TableName:                 aws.String("Items"),
		KeyConditionExpression:    aws.String("pk = :v"),
		ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
			":v": {S: aws.String("ITEM#user")},
		},
	}
	// Perform query with pagination loop; do not let LLM fabricate next token
	var results []map[string]*dynamodb.AttributeValue
	for {
		out, err := db.Query(input)
		if err != nil {
			return c.Render(500, r.JSON(map[string]string{"error": "query failed"}))
		}
		results = append(results, out.Items...)
		if out.LastEvalKey == nil {
			break
		}
		input.ExclusiveStartKey = out.LastEvalKey
	}
	// Validate each item shape before any LLM involvement
	var verified []interface{}
	for _, item := range results {
		u, err := models.UserFromDynamoDB(item)
		if err != nil {
			continue // skip malformed items instead of hallucinating
		}
		verified = append(verified, u)
	}
	return c.Render(200, r.JSON(verified))
}

4. Separate LLM usage from data retrieval

Keep LLM calls strictly downstream of verified data retrieval. If you use an LLM to generate natural language descriptions, pass only already-validated fields and explicitly disable generation of missing attributes.

// app/llm/assistant.go
package llm

import (
	"context"
	"fmt"
)

// SummarizeUser takes a verified User and produces a description; it never invents attributes.
func SummarizeUser(ctx context.Context, user *models.User) (string, error) {
	// Use only fields that are guaranteed non-zero after validation
	return fmt.Sprintf("User %s (%s) is verified: %t", user.Name, user.Email, user.Verified), nil
}

5. Enforce least privilege for DynamoDB access in Buffalo middleware

Ensure the credentials used by Buffalo to call DynamoDB have permissions only for the required actions and resources. This limits the impact if an LLM is compromised or hallucinates elevated operations.

// config/initializers/aws.go
package config

import (
	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/credentials"
	"github.com/aws/aws-sdk-go/aws/session"
)

func NewAWSSession() *session.Session {
	creds := credentials.NewStaticCredentials("ACCESS_KEY", "SECRET_KEY", "")
	sess, _ := session.NewSession(&aws.Config{
		Credentials: creds,
		Region:      aws.String("us-east-1"),
		MaxRetries:  aws.Int(3),
	})
	return sess
}