Llm Data Leakage in Gin with Dynamodb

When building a Go API with the Gin framework that uses Amazon DynamoDB, an LLM endpoint can inadvertently expose data through responses that leak information stored in DynamoDB items. This occurs when application code passes DynamoDB retrieved data into LLM prompts or passes LLM outputs back to functions that write to DynamoDB without proper sanitization or authorization checks.

Consider a Gin handler that fetches a user profile from DynamoDB and includes it in a prompt sent to an LLM for personalization. If the LLM response is returned directly to the client, sensitive fields such as email, user ID, or internal status may be present in the original DynamoDB item. Even if the LLM does not explicitly repeat the raw data, the context provided can enable prompt injection attacks where an attacker coerces the model to reveal more than intended.

An example scenario: a Gin route reads an item from a DynamoDB table using the AWS SDK, constructs a user message from item attributes, and sends it to an LLM. If the LLM endpoint is unauthenticated or the request is not scoped to least privilege, an attacker may manipulate input to extract the full item or cause the model to output credentials or keys that were stored as attributes. In another pattern, overly broad IAM permissions on the DynamoDB access used by the backend may allow the LLM-related service role to read or write items outside the intended scope, increasing exposure.

DynamoDB-specific factors that amplify risk include the storage of nested JSON-like documents and flexible schema design. A single item may contain sensitive metadata, PII, or internal flags. If these are included in prompts or reconstructed from LLM responses without validation, the data may be exposed in logs, error messages, or client responses. Additionally, inconsistent use of condition expressions or lack of fine-grained access control can lead to unintended reads that an LLM endpoint may amplify through crafted outputs.

Because middleBrick performs an LLM/AI Security scan as part of its 12 parallel checks, it can detect system prompt leakage patterns, active prompt injection attempts, and outputs that contain API keys or PII. For Gin services using DynamoDB, this means the scanner can identify whether prompts include raw attribute values, whether the LLM endpoint is unauthenticated, and whether responses expose sensitive data. Findings include severity-ranked guidance on minimizing data in prompts, applying strict input validation, and ensuring responses are sanitized before logging or transmission.

Remediation focuses on data minimization, strict access control, and output sanitization. In Gin, avoid passing entire DynamoDB items into LLM prompts. Instead, extract only the necessary, non-sensitive fields and apply strict validation. Ensure the IAM role used by the service has least-privilege permissions on the DynamoDB table and that responses are inspected before use.

Example: a Gin handler that safely reads a subset of attributes and sends them to an LLM, with explicit filtering and no sensitive fields in the prompt.

//go
package handlers

import (
    "context"
    "net/http"
    "github.com/gin-gonic/gin"
    "github.com/aws/aws-sdk-go-v2/aws"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
)

type SafeProfile struct {
    UserID string
    Name   string
}

func GetProfile(c *gin.Context) {
    userID := c.Param("user_id")
    cfg := loadConfig() // returns aws.Config
    client := dynamodb.NewFromConfig(cfg)

    out, err := client.GetItem(context.TODO(), &dynamodb.GetItemInput{
        TableName: aws.String("Users"),
        Key: map[string]types.AttributeValue{
            "user_id": &types.AttributeValueMemberS{Value: userID},
        },
        // Limit attributes to non-sensitive fields only
        ProjectionExpression: aws.String("user_id,name"),
    })
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": "unable to fetch profile"})
        return
    }

    var safe SafeProfile
    if out.Item == nil {
        c.JSON(http.StatusNotFound, gin.H{"error": "not found"})
        return
    }
    if v, ok := out.Item["user_id"].(*types.AttributeValueMemberS); ok {
        safe.UserID = v.Value
    }
    if v, ok := out.Item["name"].(*types.AttributeValueMemberS); ok {
        safe.Name = v.Value
    }

    // Use only safe, non-sensitive data in LLM prompt
    prompt := "Provide a greeting for user: " + safe.Name
    llmResp, err := callLLM(prompt) // implement with appropriate client and context
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": "LLM unavailable"})
        return
    }

    // Sanitize LLM output before any logging or further use
    c.JSON(http.StatusOK, gin.H{"greeting": llmResp})
}

Ensure the IAM role associated with this service grants dynamodb:GetItem on the specific table and only for the required attributes. Avoid wildcard permissions. Rotate credentials regularly and use environment variables or secure secret stores for configuration.

Additionally, apply strict input validation on userID to prevent injection or traversal attacks, and ensure LLM endpoints are authenticated and scoped. Use middleware in Gin to validate and sanitize inputs before they reach handlers. By combining least-privilege DynamoDB access, data minimization, and output sanitization, the risk of LLM-driven data leakage is significantly reduced.